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      name:  <unnamed>
       log:  C:\Users\hyuns\Desktop\[Material] Replication (v.3_single_do_file)\새 폴더\[Text] Log File (Tables and Figur
> es).log
  log type:  text
 opened on:  20 Nov 2025, 18:28:37

. 
. ********************************************************************************
. *                                                                              *
. *   Reproduction do file for                                                                                           
> *
. *      Author  : Ha, Hyunsoo and Park, Jaesuk                                      *
. *      Title   : Coercive Origin of Banking Giants                             *
. *                                                                              *
. *   Table of Contents                                                          *
. *                                                                              *
. *       0. General Instructions                                                *
. *                                                                              *
. *       I.  Main Manuscript: Main Tables                                       *
. *          ——————————————————————————————————————————————————————————————————  *      
. *           Table 1.     Effects of Sanctions on Bank Competition              *
. *           Table 2.     Overview of Robustness Tests: Pre-1996 Excluded       *
. *           Table 3.     Overview of Robustness Tests: Pre-1996 Included       *
. *          ——————————————————————————————————————————————————————————————————  *      
. *                                                                              *
. *      II.  Main Manuscript: Figures                                           *
. *          ——————————————————————————————————————————————————————————————————  *      
. *           Figure 1a.   Unobserved Pre-1996 Trend                             *
. *           Figure 1b.   Inclusion of Long-lasting Sanctions                   *
. *           Figure 2.    The Lerner Index of the Iranian Banking Industry      *
. *          ——————————————————————————————————————————————————————————————————  *      
. *                                                                              *
. *     III.  Online Appendix: Summary Statistics and Robustness Tests           *
. *           —————————————————————————————————————————————————————————————————  *
. *                          (1)          (2)            (3)            (4)      *
. *                        Pre-1996      Sample    Indep. Variable   Estimation  *
. *           —————————————————————————————————————————————————————————————————  *  
. *           Appx. 3.     Escluded    Summary Statistics                        *
. *           Appx. 4.     Excluded    Full            None           OLS        *
. *           Appx. 5.     Excluded    Full            Duration       OLS        *
. *           Appx. 6.     Excluded    Full            Dummy          OLS        *
. *           Appx. 7.     Excluded    Sanc. States    Duration       Heckman    *
. *           Appx. 8.     Excluded    1-Year Lag      Duration       OLS        *
. *           Appx. 9.     Excluded    Diff: 5-7 Yr    Duration       OLS        *
. *           Appx. 10.    Excluded    Diff: 6-8 Yr    Duration       OLS        *
. *           Appx. 11.    Excluded    Diff: 7-9 Yr    Duration       OLS        *
. *           Appx. 12.    Included    Summary Statistics                        * 
. *           Appx. 13.    Included    Full            Duration       OLS        *
. *           Appx. 14.    Included    Full            None           OLS        *
. *           Appx. 15.    Included    Full            Duration       OLS        *
. *           Appx. 16.    Included    Full            Dummy          OLS        *
. *           Appx. 17.    Included    Sanc. States    Duration       Heckman    *
. *           Appx. 18.    Included    1-Year Lag      Duration       OLS        *
. *           Appx. 19.    Included    Diff: 5-7 Yr    Duration       OLS        *
. *           Appx. 20.    Included    Diff: 6-8 Yr    Duration       OLS        *
. *           Appx. 21.    Included    Diff: 7-9 Yr    Duration       OLS        *
. *           —————————————————————————————————————————————————————————————————  *  
. *                                                                              *
. ********************************************************************************
. 
. 
. ********************************************************************************
. *** 0. General Instructions ****************************************************
. ********************************************************************************
.  
.   * (1) STATA Version Requirement
.   *     The code in this replication do-file was written and tested in 
.   *     STATA 19. Users are strongly encouraged to run the replication 
.   *     using STATA 19 to ensure full compatibility and intended 
.   *     functionality.
.   
.   * (2) Required Packages for Download
.   *     The replication code uses several user-written packages that must be 
.   *     installed before running the analyses. You can install all required 
.   *     packages by running the commands below:
.  
.         ssc install outreg2, replace
checking outreg2 consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install estout, replace
checking estout consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install fitstat, replace
checking fitstat consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install ftools, replace
checking ftools consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install reghdfe, replace
checking reghdfe consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install asdoc, replace
checking asdoc consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install distinct, replace
checking distinct consistency and verifying not already installed...
all files already exist and are up to date.

.         ssc install rangestat, replace
checking rangestat consistency and verifying not already installed...
all files already exist and are up to date.

. 
.   * (3) Notes on Running the Code
.   *     There are two ways to run the replication files:
.   *
.   *     i. To replicate all tables and figures, simply run the entire 
.   *        do-file from start to finish in one run.
.   *
.   *     ii. To replicate a specific table or figure, navigate to the 
.   *         relevant section (e.g., "Table 1" or "Appendix 13") and 
.   *         run that block along with the preparatory steps listed 
.   *         at the beginning of each section.           
.                 
.                 
. ********************************************************************************
. *** I. Main Manuscript: Main Tables ********************************************
. ********************************************************************************                
.                 
. ** Table 1. Effects of Sanctions on Bank Competition ***************************        
.                 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

.                 
.   * Model (1)
.         reghdfe lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   5,   1859) =       9.65
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5274
                                                  Adj R-squared   =     0.4920
                                                  Within R-sq.    =     0.0116
                                                  Root MSE        =     0.1021

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    sanc_dur |  -.0000862   .0017721    -0.05   0.961    -.0035617    .0033894
sanc_postdur |  -.0066909   .0015779    -4.24   0.000    -.0097856   -.0035962
   sanc_type |  -.0012593   .0051038    -0.25   0.805     -.011269    .0087504
  sanc_state |  -.0060893   .0016551    -3.68   0.000    -.0093353   -.0028434
    sanc_org |  -.0006623   .0141713    -0.05   0.963    -.0284556     .027131
       _cons |   .2620141   .0027263    96.10   0.000      .256667    .2673611
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_1

. 
.   * Model (2)   
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       7.33
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5353
                                                  Adj R-squared   =     0.4997
                                                  Within R-sq.    =     0.0281
                                                  Root MSE        =     0.1013

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
       sanc_dur |  -.0005698   .0017656    -0.32   0.747    -.0040325    .0028929
   sanc_postdur |  -.0066829   .0015452    -4.32   0.000    -.0097136   -.0036523
      sanc_type |   .0000703   .0051435     0.01   0.989    -.0100173    .0101579
     sanc_state |  -.0062378   .0016941    -3.68   0.000    -.0095604   -.0029152
       sanc_org |    .000188   .0142474     0.01   0.989    -.0277547    .0281307
econ_change_gdp |   .0032704   .0009159     3.57   0.000     .0014742    .0050667
       econ_fin |  -.0057115   .0069988    -0.82   0.415    -.0194379    .0080149
 econ_asset_gdp |  -.0002746   .0001413    -1.94   0.052    -.0005518    2.46e-06
          _cons |    .267923   .0095207    28.14   0.000     .2492506    .2865953
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_2

. 
.   * Model (3)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   7,   1857) =      10.92
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5330
                                                  Adj R-squared   =     0.4975
                                                  Within R-sq.    =     0.0233
                                                  Root MSE        =     0.1015

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0069626   .0021164     3.29   0.001     .0028118    .0111133
   sanc_fpostdur |  -.0117718   .0025935    -4.54   0.000    -.0168583   -.0066853
    sanc_nonfdur |  -.0015808   .0017931    -0.88   0.378    -.0050976     .001936
sanc_nonfpostdur |  -.0028627   .0017904    -1.60   0.110    -.0063741    .0006488
       sanc_type |   -.006673   .0047062    -1.42   0.156     -.015903    .0025569
      sanc_state |   -.005229   .0015773    -3.32   0.001    -.0083224   -.0021356
        sanc_org |  -.0155962   .0142195    -1.10   0.273     -.043484    .0122916
           _cons |   .2647703   .0027428    96.53   0.000      .259391    .2701495
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_dur_ols_3

. 
.   * Model (4)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_4

.         
.   * Export regression table
.     esttab full_dur_ols_1  full_dur_ols_2  ///
>                full_dur_ols_3  full_dur_ols_4  ///
>                using "[Table 1] full_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace               
(file [Table 1] full_dur_ols.rtf not found)
(output written to [Table 1] full_dur_ols.rtf)

.                         
.                         
. ** Table 2. Overview of Robustness Tests: Pre-1996 Excluded ********************        
.                 
.   *  Table 2 in the main manuscript is a "manually compiled" summary table
.   *  that reports selected coefficient estimates for key independent 
.   *  variables from the robustness tests. 
.   *
.   *  All underlying regression models are fully reproduced in Appendix 
.   *  5–11 using the code in this do-file. Users can verify coefficients 
.   *  reported in Table 2 by consulting the corresponding appendix output.
.   *  The mapping between Table 2 and the appendix tables is summarized below:
.   *  
.   *  ——————————————————————————————————————————————————————————————————————— 
.   *                                                 (1)           (2)
.   *                                               Appx No.      Model No.
.   *  ——————————————————————————————————————————————————————————————————————— 
.   *   R1: Alternative Control (GDPPC)                5            1, 3
.   *   R2: Alternative Control (Total GDP)            5            2, 4
.   *   R3: Imposition Dummy                           6            2, 4
.   *   R4: Heckman Selection Model                    7            2, 4
.   *   R5: 1-Year Lagged Variables                    8            2, 4
.   *   R6: Long-run Avg. Change (5-7 Yr)              9            2, 4
.   *   R7: Long-run Avg. Change (6-8 Yr)              10           2, 4
.   *   R8: Long-run Avg. Change (7-9 Yr)              11           2, 4
.   *  ———————————————————————————————————————————————————————————————————————
.                 
.                 
. ** Table 3. Overview of Robustness Tests: Pre-1996 Included ********************        
.                 
.   *  Table 3 in the main manuscript is a "manually compiled" summary table
.   *  that reports selected coefficient estimates for key independent 
.   *  variables from the robustness tests. 
.   *
.   *  All underlying regression models are fully reproduced in Appendix 
.   *  13, 15–21 using the code in this do-file. Users can verify coefficients 
.   *  reported in Table 3 by consulting the corresponding appendix output.
.   *  The mapping between Table 3 and the appendix tables is summarized below:
.   *  
.   *  ——————————————————————————————————————————————————————————————————————— 
.   *                                                 (1)           (2)
.   *                                               Appx No.      Model No.
.   *  ——————————————————————————————————————————————————————————————————————— 
.   *   Main Estimation                                13           2, 4
.   *   R1: Alternative Control (GDPPC)                15           1, 3
.   *   R2: Alternative Control (Total GDP)            15           2, 4
.   *   R3: Imposition Dummy                           16           2, 4
.   *   R4: Heckman Selection Model                    17           2, 4
.   *   R5: 1-Year Lagged Variables                    18           2, 4
.   *   R6: Long-run Avg. Change (5-7 Yr)              19           2, 4
.   *   R7: Long-run Avg. Change (6-8 Yr)              20           2, 4
.   *   R8: Long-run Avg. Change (7-9 Yr)              21           2, 4
.   *  ———————————————————————————————————————————————————————————————————————
.                                 
.                 
. ********************************************************************************
. *** II. Main Manuscript: Figures ***********************************************
. ********************************************************************************                        
.                 
. ** Figure 1a. Unobserved Pre-1996 Trend ****************************************
. 
.   *  Figure 1a is a conceptual illustration created in LaTeX, not an empirical
.   *  graph. As such, there is no corresponding Stata code for replication. 
. 
.   
. ** Figure 1b. Inclusion of Long-lasting Sanctions ******************************
.                 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Preparatory Steps (7): Graph setting
.     graph set window fontface "Helvetica"

. 
.   * Figure 1b           
.     histogram sanc_dur if sanc_dur > 19, frequency ///
>        width(1) ///
>        start(20) ///
>        color(gs13) ///
>        lcolor(gs8) ///
>        title("") ///
>        xtitle("Sanctions Duration: All Sanctions", size(small) margin(t+2)) ///
>        ytitle("Frequency", size(small) margin(t+4)) ///
>        ylabel(2(2)10, nogrid notick labsize(small)) ///
>        xlabel(, nogrid notick labsize(small)) ///
>        xscale(range(20 65)) ///
>        graphregion(color(white)) ///
>        aspectratio(1) ///
>        plotregion(margin(0 0 0 0)) ///
>        legend(off)
(bin=45, start=20, width=1)

. 
. 
. ** Figure 2. The Lerner Index of the Iranian Banking Industry ******************
.         
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace                     

.                 
.   * Preparatory Steps (3): Graph setting
.     graph set window fontface "Helvetica"               

.                 
.   * Figure 2    
.         twoway (line lerner year if iso3 == "IRN" & inrange(year, 2000, 2010), ///
>             lcolor(black) lwidth(medthin)), ///
>                         xline(2001, lpattern(dash) lcolor(black)) ///
>                         title("") ///
>                         xtitle("Year", size(small) margin(t+2)) ///
>                         ytitle("Lerner Index", size(small) margin(t+6)) ///
>                         xlabel(2001 2003 2005 2007 2009, labsize(small) nogrid notick) ///
>                         ylabel(0.1(0.1)0.6, labsize(small) nogrid notick) ///
>                         xscale(range(2000 2010)) ///
>                         graphregion(color(white)) ///
>                         aspectratio(1) ///
>                         plotregion(margin(0 0 0 0)) ///
>                         legend(off)             

. 
.                         
. ********************************************************************************                                
. * III. Online Appendix: Summary Statistics and Robustness Tests ****************        
. ********************************************************************************                        
.                         
. ** Appx. 3. Pre-1996: Excluded / Summary Statistics ****************************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

.         
.   *     Appx. 3. Summary Statistics
.     asdoc sum lerner ///
>                   sanc_dur sanc_postdur sanc_dur_dummy ///
>                           sanc_fdur sanc_fpostdur sanc_fdur_dummy ///
>                           sanc_nonfdur sanc_nonfpostdur sanc_nonfdur_dummy ///
>                           sanc_type sanc_state sanc_org ///
>                           econ_change_gdp econ_lgdppc econ_lgdp econ_fin econ_asset_gdp ///
>                           pol_polity2dem ///
>                           econ_fdi_gdp econ_trade_gdp, ///
>                           save([Appendix 3] Summary Statistics.doc) replace     

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      lerner |      1,999    .2557181    .1432471   -1.60869    1.07559
    sanc_dur |      1,999    1.091546     2.90888          0         18
sanc_postdur |      1,999    .3881941    1.753698          0         16
sanc_dur_d~y |      1,999    .1850925    .3884698          0          1
   sanc_fdur |      1,999    .4172086    1.707612          0         17
-------------+---------------------------------------------------------
sanc_fpost~r |      1,999    .2331166    1.386581          0         16
sanc_fdur_~y |      1,999    .0890445    .2848793          0          1
sanc_nonfdur |      1,999    .9864932    2.777955          0         18
sanc_nonfp~r |      1,999    .3476738    1.600076          0         15
sanc_nonfd~y |      1,999    .1690845    .3749204          0          1
-------------+---------------------------------------------------------
   sanc_type |      1,999    .3991996    1.069515          0          6
  sanc_state |      1,999    .4997499    2.079471          0         16
    sanc_org |      1,999    .0885443    .2841559          0          1
econ_chang~p |      1,999    4.015137    4.089033  -17.00469       34.5
 econ_lgdppc |      1,999    8.564123    1.553591   4.704661   11.72544
-------------+---------------------------------------------------------
   econ_lgdp |      1,999    24.88218    1.900457   20.27051   30.49611
    econ_fin |      1,999    .2716358    .4449144          0          1
econ_asset~p |      1,999    63.70258    47.37141   .4382711   305.2436
pol_polity~m |      1,845    .6769648    .4677628          0          1
econ_fdi_gdp |      1,989    .0586874    .2019476  -.5753231   4.490828
-------------+---------------------------------------------------------
econ_trade~p |      1,869    .8477326    .5590917   .0303081   4.426249
Click to Open File:  [Appendix 3] Summary Statistics.doc

.         
. 
. ** Appx. 4. Pre-1996: Excluded / Sample: Full / IV: None / OLS *****************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

.         
.   * Model (1)
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   3,   1861) =      10.26
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5252
                                                  Adj R-squared   =     0.4902
                                                  Within R-sq.    =     0.0070
                                                  Root MSE        =     0.1023

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   sanc_type |   .0008373   .0042898     0.20   0.845     -.007576    .0092506
  sanc_state |  -.0061071   .0016338    -3.74   0.000    -.0093114   -.0029028
    sanc_org |   .0018697   .0139664     0.13   0.894    -.0255218    .0292612
       _cons |   .2582704   .0025452   101.47   0.000     .2532786    .2632621
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_1

. 
.   * Model (2)   
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   6,   1858) =       7.30
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5331
                                                  Adj R-squared   =     0.4979
                                                  Within R-sq.    =     0.0236
                                                  Root MSE        =     0.1015

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
      sanc_type |    .001628   .0043937     0.37   0.711    -.0069891    .0102451
     sanc_state |  -.0062353   .0016748    -3.72   0.000      -.00952   -.0029505
       sanc_org |   .0024114   .0140394     0.17   0.864    -.0251233     .029946
econ_change_gdp |   .0032321   .0009103     3.55   0.000     .0014468    .0050175
       econ_fin |  -.0056277   .0070112    -0.80   0.422    -.0193783    .0081229
 econ_asset_gdp |   -.000289   .0001417    -2.04   0.042    -.0005669   -.0000111
          _cons |   .2649307   .0095516    27.74   0.000     .2461976    .2836637
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_2

. 
.   * Model (3)   
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   3,   1861) =      10.26
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5252
                                                  Adj R-squared   =     0.4902
                                                  Within R-sq.    =     0.0070
                                                  Root MSE        =     0.1023

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   sanc_type |   .0008373   .0042898     0.20   0.845     -.007576    .0092506
  sanc_state |  -.0061071   .0016338    -3.74   0.000    -.0093114   -.0029028
    sanc_org |   .0018697   .0139664     0.13   0.894    -.0255218    .0292612
       _cons |   .2582704   .0025452   101.47   0.000     .2532786    .2632621
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_no_ols_3

. 
.   * Model (4)
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   6,   1858) =       7.30
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5331
                                                  Adj R-squared   =     0.4979
                                                  Within R-sq.    =     0.0236
                                                  Root MSE        =     0.1015

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
      sanc_type |    .001628   .0043937     0.37   0.711    -.0069891    .0102451
     sanc_state |  -.0062353   .0016748    -3.72   0.000      -.00952   -.0029505
       sanc_org |   .0024114   .0140394     0.17   0.864    -.0251233     .029946
econ_change_gdp |   .0032321   .0009103     3.55   0.000     .0014468    .0050175
       econ_fin |  -.0056277   .0070112    -0.80   0.422    -.0193783    .0081229
 econ_asset_gdp |   -.000289   .0001417    -2.04   0.042    -.0005669   -.0000111
          _cons |   .2649307   .0095516    27.74   0.000     .2461976    .2836637
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_4 

.         
.   * Export regression table
.     esttab full_no_ols_1  full_no_ols_2  ///
>                full_no_ols_3  full_no_ols_4  ///
>                using "[Appendix 4] full_no_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace       
(file [Appendix 4] full_no_ols.rtf not found)
(output written to [Appendix 4] full_no_ols.rtf)

. 
.         
. ** Appx. 5. Pre-1996: Excluded / Sample: Full / IV: Duration / OLS *************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Model (1)
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdppc econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       6.70
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5303
                                                  Adj R-squared   =     0.4944
                                                  Within R-sq.    =     0.0178
                                                  Root MSE        =     0.1019

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      sanc_dur |  -.0001976   .0017623    -0.11   0.911    -.0036538    .0032587
  sanc_postdur |  -.0063941   .0015422    -4.15   0.000    -.0094188   -.0033695
     sanc_type |  -.0009176   .0051121    -0.18   0.858    -.0109436    .0091084
    sanc_state |  -.0059623   .0016678    -3.58   0.000    -.0092332   -.0026914
      sanc_org |  -.0013378    .014285    -0.09   0.925    -.0293542    .0266786
   econ_lgdppc |  -.0098091   .0124793    -0.79   0.432    -.0342841    .0146659
      econ_fin |  -.0118211   .0071284    -1.66   0.097    -.0258016    .0021594
econ_asset_gdp |  -.0003527   .0001585    -2.23   0.026    -.0006635   -.0000418
         _cons |   .3715649   .1045752     3.55   0.000     .1664674    .5766623
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_gdppc_1

. 
.   * Model (2)   
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       6.62
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5303
                                                  Adj R-squared   =     0.4943
                                                  Within R-sq.    =     0.0176
                                                  Root MSE        =     0.1019

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      sanc_dur |  -.0002042    .001759    -0.12   0.908    -.0036541    .0032457
  sanc_postdur |  -.0064319    .001543    -4.17   0.000    -.0094582   -.0034057
     sanc_type |  -.0008755   .0051058    -0.17   0.864    -.0108892    .0091382
    sanc_state |   -.005999   .0016651    -3.60   0.000    -.0092646   -.0027334
      sanc_org |  -.0013178   .0142711    -0.09   0.926    -.0293069    .0266713
     econ_lgdp |  -.0072823   .0119856    -0.61   0.544     -.030789    .0162243
      econ_fin |  -.0115778   .0072849    -1.59   0.112    -.0258652    .0027097
econ_asset_gdp |  -.0003595   .0001562    -2.30   0.022    -.0006659    -.000053
         _cons |   .4691465   .2966223     1.58   0.114    -.1126019    1.050895
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_lgdp_1   

. 
.   * Model (3)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdppc econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       8.03
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5358
                                                  Adj R-squared   =     0.4998
                                                  Within R-sq.    =     0.0293
                                                  Root MSE        =     0.1013

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0062224   .0021803     2.85   0.004     .0019463    .0104985
   sanc_fpostdur |  -.0121455   .0027007    -4.50   0.000    -.0174422   -.0068488
    sanc_nonfdur |  -.0014264   .0017841    -0.80   0.424    -.0049254    .0020726
sanc_nonfpostdur |  -.0025488   .0017588    -1.45   0.147    -.0059983    .0009006
       sanc_type |  -.0062284    .004718    -1.32   0.187    -.0154815    .0030247
      sanc_state |   -.005087      .0016    -3.18   0.002    -.0082251    -.001949
        sanc_org |  -.0159114   .0142979    -1.11   0.266    -.0439531    .0121304
     econ_lgdppc |  -.0078481   .0125121    -0.63   0.531    -.0323873    .0166912
        econ_fin |   -.012416   .0071721    -1.73   0.084    -.0264822    .0016502
  econ_asset_gdp |  -.0003433   .0001592    -2.16   0.031    -.0006556    -.000031
           _cons |   .3571355   .1045709     3.42   0.001     .1520465    .5622246
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_gdppc_2

. 
.   * Model (4)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       7.89
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5357
                                                  Adj R-squared   =     0.4997
                                                  Within R-sq.    =     0.0291
                                                  Root MSE        =     0.1013

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0062092    .002187     2.84   0.005       .00192    .0104984
   sanc_fpostdur |  -.0121741   .0027409    -4.44   0.000    -.0175497   -.0067985
    sanc_nonfdur |    -.00146   .0017816    -0.82   0.413    -.0049541    .0020341
sanc_nonfpostdur |  -.0025833   .0017671    -1.46   0.144    -.0060489    .0008824
       sanc_type |  -.0061684   .0047157    -1.31   0.191    -.0154169    .0030802
      sanc_state |  -.0051293   .0015978    -3.21   0.001    -.0082629   -.0019957
        sanc_org |  -.0157219    .014278    -1.10   0.271    -.0437246    .0122809
       econ_lgdp |  -.0032673   .0121969    -0.27   0.789    -.0271885    .0206539
        econ_fin |   -.011798   .0073045    -1.62   0.106    -.0261238    .0025279
  econ_asset_gdp |  -.0003509   .0001571    -2.23   0.026     -.000659   -.0000429
           _cons |    .371579   .3014329     1.23   0.218    -.2196045    .9627626
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_lgdp_2

.         
.   * Export regression table
.     esttab full_dur_ols_gdppc_1 full_dur_ols_lgdp_1  ///
>                full_dur_ols_gdppc_2 full_dur_ols_lgdp_2 ///
>                using "[Appendix 5] full_dur_ols_lgdppc_lgdp.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_lgdppc econ_lgdp econ_fin econ_asset_gdp) replace         
(file [Appendix 5] full_dur_ols_lgdppc_lgdp.rtf not found)
(output written to [Appendix 5] full_dur_ols_lgdppc_lgdp.rtf)

.         
.         
. ** Appx. 6. Pre-1996: Excluded / Sample: Full / IV: Dummy / OLS ****************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Model (1)                   
.         reghdfe lerner sanc_dur_dummy ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   4,   1860) =       7.63
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5253
                                                  Adj R-squared   =     0.4901
                                                  Within R-sq.    =     0.0073
                                                  Root MSE        =     0.1023

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
sanc_dur_dummy |  -.0100377   .0138132    -0.73   0.468    -.0371287    .0170532
     sanc_type |   .0028159   .0051956     0.54   0.588    -.0073739    .0130056
    sanc_state |  -.0062256   .0016685    -3.73   0.000    -.0094979   -.0029533
      sanc_org |   .0051738   .0154566     0.33   0.738    -.0251403    .0354879
         _cons |   .2591051   .0025979    99.73   0.000     .2540099    .2642003
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_1

. 
.   * Model (2)   
.         reghdfe lerner sanc_dur_dummy ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   7,   1857) =       6.34
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5333
                                                  Adj R-squared   =     0.4979
                                                  Within R-sq.    =     0.0240
                                                  Root MSE        =     0.1015

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
 sanc_dur_dummy |  -.0111799   .0138304    -0.81   0.419    -.0383046    .0159449
      sanc_type |   .0038227    .005287     0.72   0.470    -.0065464    .0141919
     sanc_state |   -.006358   .0017077    -3.72   0.000    -.0097072   -.0030088
       sanc_org |     .00608    .015404     0.39   0.693     -.024131     .036291
econ_change_gdp |   .0032301   .0009108     3.55   0.000     .0014438    .0050163
       econ_fin |  -.0060139   .0070515    -0.85   0.394    -.0198437    .0078159
 econ_asset_gdp |  -.0002884   .0001416    -2.04   0.042    -.0005661   -.0000107
          _cons |   .2659368    .009776    27.20   0.000     .2467637    .2851099
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_2

.         
.   * Model (3)           
.         reghdfe lerner sanc_fdur_dummy sanc_nonfdur_dummy ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   5,   1859) =       8.18
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5275
                                                  Adj R-squared   =     0.4922
                                                  Within R-sq.    =     0.0119
                                                  Root MSE        =     0.1021

------------------------------------------------------------------------------------
                   |               Robust
            lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
   sanc_fdur_dummy |   .0474879    .017131     2.77   0.006     .0138899    .0810859
sanc_nonfdur_dummy |  -.0111654   .0133763    -0.83   0.404    -.0373996    .0150689
         sanc_type |  -.0030526   .0056736    -0.54   0.591      -.01418    .0080747
        sanc_state |  -.0055292   .0016827    -3.29   0.001    -.0088294    -.002229
          sanc_org |  -.0153014   .0177531    -0.86   0.389    -.0501195    .0195167
             _cons |   .2587142   .0025789   100.32   0.000     .2536563    .2637721
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_imposition_ols_3

. 
.   * Model (4)                   
.         reghdfe lerner sanc_fdur_dummy sanc_nonfdur_dummy ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       6.82
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5350
                                                  Adj R-squared   =     0.4994
                                                  Within R-sq.    =     0.0276
                                                  Root MSE        =     0.1013

------------------------------------------------------------------------------------
                   |               Robust
            lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
   sanc_fdur_dummy |   .0418268   .0170826     2.45   0.014     .0083236      .07533
sanc_nonfdur_dummy |  -.0124086   .0134219    -0.92   0.355    -.0387321     .013915
         sanc_type |  -.0012086   .0057649    -0.21   0.834    -.0125149    .0100978
        sanc_state |  -.0057837    .001719    -3.36   0.001    -.0091551   -.0024123
          sanc_org |  -.0123894   .0177142    -0.70   0.484    -.0471313    .0223525
   econ_change_gdp |   .0031855   .0009127     3.49   0.000     .0013954    .0049757
          econ_fin |  -.0051217   .0069849    -0.73   0.464    -.0188208    .0085775
    econ_asset_gdp |  -.0002732   .0001416    -1.93   0.054    -.0005508    4.53e-06
             _cons |   .2645631   .0096711    27.36   0.000     .2455958    .2835304
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_4 

.         
.   * Export regression table     
.     esttab full_imposition_ols_1  full_imposition_ols_2  ///
>            full_imposition_ols_3  full_imposition_ols_4 ///     
>                using "[Appendix 6] full_imposition_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy  ///
>                          sanc_fdur_dummy sanc_nonfdur_dummy ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace                       
(file [Appendix 6] full_imposition_ols.rtf not found)
(output written to [Appendix 6] full_imposition_ols.rtf)

. 
. 
. ** Appx. 7. Pre-1996: Excluded / Sample: Sanc. States / IV: Duration / Heckman *
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

.         
.   * Preparatory Steps (7): Restrict sample to the countries that experienced sanctions
.     egen exp_sanc = max(sanc_dur_dummy), by(iso3_num)

.     keep if exp_sanc == 1
(1,123 observations deleted)

. 
.   * Model (1)   
.     heckman lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                         
>                                  
note: sanc_postdur omitted because of collinearity.

Iteration 0:  Log pseudolikelihood =  147.12383  
Iteration 1:  Log pseudolikelihood =  147.32575  
Iteration 2:  Log pseudolikelihood =  149.05967  
Iteration 3:  Log pseudolikelihood =  149.08387  
Iteration 4:  Log pseudolikelihood =  149.08389  

Heckman selection model                         Number of obs     =        738
(regression model with sample selection)              Selected    =        302
                                                      Nonselected =        436

                                                Wald chi2(61)     =          .
Log pseudolikelihood =  149.0839                Prob > chi2       =          .

--------------------------------------------------------------------------------
               |               Robust
               | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
lerner         |
      sanc_dur |   .0089121   .0061772     1.44   0.149     -.003195    .0210191
  sanc_postdur |          0  (omitted)
     sanc_type |   .0092649   .0114796     0.81   0.420    -.0132346    .0317644
    sanc_state |  -.0038307   .0052009    -0.74   0.461    -.0140243    .0063628
      sanc_org |  -.0175298   .0388793    -0.45   0.652    -.0937318    .0586722
               |
      iso3_num |
          ARG  |  -.3958023    .159971    -2.47   0.013    -.7093398   -.0822648
          AZE  |   .0347727   .1221804     0.28   0.776    -.2046964    .2742418
          BEN  |  -.1368171   .0747102    -1.83   0.067    -.2832463    .0096122
          BGR  |  -.0601482    .071987    -0.84   0.403    -.2012401    .0809437
          BLR  |  -.1865147   .0721175    -2.59   0.010    -.3278624   -.0451669
          BOL  |  -.0621689   .1078674    -0.58   0.564     -.273585    .1492473
          BRA  |  -.1446429   .0690414    -2.10   0.036    -.2799615   -.0093243
          CAN  |  -.2257303   .0736441    -3.07   0.002    -.3700701   -.0813904
          CHE  |  -.3102546   .1084187    -2.86   0.004    -.5227514   -.0977577
          CIV  |  -.1675922   .0785078    -2.13   0.033    -.3214646   -.0137198
          COD  |  -.3592365   .0453727    -7.92   0.000    -.4481653   -.2703076
          COL  |  -.1249984   .0528383    -2.37   0.018    -.2285595   -.0214372
          CRI  |  -.2629932   .0661777    -3.97   0.000    -.3926992   -.1332873
          DOM  |  -.1510416   .1071815    -1.41   0.159    -.3611134    .0590303
          ECU  |  -.0995638    .116037    -0.86   0.391    -.3269922    .1278645
          FRA  |  -.3401226   .0637486    -5.34   0.000    -.4650675   -.2151777
          GEO  |  -.1401174   .0839041    -1.67   0.095    -.3045665    .0243316
          HND  |  -.1117495   .0760838    -1.47   0.142    -.2608711    .0373721
          HRV  |   -.148526   .0704842    -2.11   0.035    -.2866725   -.0103796
          IDN  |  -.2110983   .0809032    -2.61   0.009    -.3696656   -.0525309
          IND  |  -.1943636   .0647246    -3.00   0.003    -.3212215   -.0675057
          IRL  |  -.2204612   .0651872    -3.38   0.001    -.3482258   -.0926967
          IRN  |   -.373646   .0560382    -6.67   0.000    -.4834789   -.2638132
          ISR  |   -.111239   .1219994    -0.91   0.362    -.3503534    .1278754
          ITA  |   -.136965   .0533188    -2.57   0.010    -.2414679   -.0324621
          KEN  |   .0193277   .0907934     0.21   0.831    -.1586241    .1972795
          LBN  |  -.2397477   .0514858    -4.66   0.000     -.340658   -.1388374
          LTU  |  -.0176589   .1173866    -0.15   0.880    -.2477325    .2124146
          MDA  |  -.0713308   .0935492    -0.76   0.446     -.254684    .1120223
          MLI  |  -.1712611   .0945065    -1.81   0.070    -.3564904    .0139682
          MWI  |  -.1265967   .0531503    -2.38   0.017    -.2307695   -.0224239
          NGA  |  -.1882558   .0673382    -2.80   0.005    -.3202361   -.0562754
          NOR  |   .0410826   .1030993     0.40   0.690    -.1609884    .2431536
          NPL  |  -.0970183   .0786527    -1.23   0.217    -.2511748    .0571381
          PAK  |  -.2662194   .0472931    -5.63   0.000    -.3589122   -.1735265
          PAN  |   -.032955   .1076884    -0.31   0.760    -.2440204    .1781104
          PHL  |  -.1938135   .0567817    -3.41   0.001    -.3051037   -.0825234
          PRY  |  -.1377985   .0941223    -1.46   0.143    -.3222748    .0466777
          RUS  |  -.2861259   .1053027    -2.72   0.007    -.4925153   -.0797365
          SDN  |   -.176391   .0458807    -3.84   0.000    -.2663156   -.0864664
          SLE  |  -.0800261   .0681768    -1.17   0.240    -.2136501     .053598
          THA  |  -.0451421    .078057    -0.58   0.563    -.1981311    .1078468
          TUN  |   .0647167   .0973036     0.67   0.506    -.1259949    .2554282
          UKR  |  -.0854441   .0943994    -0.91   0.365    -.2704636    .0995754
          USA  |  -.1558249   .0733095    -2.13   0.034    -.2995088    -.012141
          VEN  |  -.1077595   .0845442    -1.27   0.202     -.273463     .057944
               |
          year |
         1998  |   .0393871   .0648405     0.61   0.544     -.087698    .1664722
         1999  |   .0047812   .1028453     0.05   0.963    -.1967919    .2063543
         2000  |   .0925345   .0768692     1.20   0.229    -.0581264    .2431954
         2001  |   .0744106   .0589845     1.26   0.207    -.0411968    .1900181
         2002  |   .1274061   .0561372     2.27   0.023     .0173791     .237433
         2003  |   .0850996   .0661496     1.29   0.198    -.0445512    .2147504
         2004  |   .1316707   .0602895     2.18   0.029     .0135055     .249836
         2005  |   .1132911   .0629609     1.80   0.072    -.0101099    .2366922
         2006  |   .0951146   .0668668     1.42   0.155    -.0359419     .226171
         2007  |   .0663704    .074247     0.89   0.371     -.079151    .2118918
         2008  |   .0485945   .0770122     0.63   0.528    -.1023467    .1995357
         2009  |   .0502698   .0806245     0.62   0.533    -.1077513    .2082908
         2010  |   .0466647    .085696     0.54   0.586    -.1212964    .2146258
         2011  |   .0609289   .0892032     0.68   0.495    -.1139061     .235764
         2012  |   .0627893   .0933346     0.67   0.501    -.1201431    .2457218
         2013  |   .0608024   .0988351     0.62   0.538    -.1329109    .2545158
         2014  |   .0584888   .1040397     0.56   0.574    -.1454253    .2624029
               |
         _cons |   .3014313   .0774843     3.89   0.000     .1495649    .4532978
---------------+----------------------------------------------------------------
sanc_dur_dummy |
sanc_dur_dummy |
           L1. |   2.171489   .1816178    11.96   0.000     1.815524    2.527453
               |
pol_polity2dem |  -.9545467   .2733317    -3.49   0.000    -1.490267   -.4188264
   econ_lgdppc |   -.155558   .3137344    -0.50   0.620    -.7704661    .4593501
  econ_fdi_gdp |  -.7130694   2.140761    -0.33   0.739    -4.908883    3.482744
econ_trade_gdp |  -.2246103   .6923609    -0.32   0.746    -1.581613    1.132392
               |
      iso3_num |
          ARG  |   .8401657   .8144299     1.03   0.302    -.7560876    2.436419
          AZE  |  -.2993257   .6610247    -0.45   0.651     -1.59491    .9962588
          BEN  |   1.175165   .9281827     1.27   0.205    -.6440394     2.99437
          BGR  |   .8022606   .7382808     1.09   0.277    -.6447431    2.249264
          BLR  |   5.863373   .4904152    11.96   0.000     4.902177    6.824569
          BOL  |   .8398396   .6761738     1.24   0.214    -.4854368    2.165116
          BRA  |   .5349376   .9581799     0.56   0.577    -1.343061    2.412936
          CAN  |    2.22122   1.066514     2.08   0.037     .1308912     4.31155
          CHE  |   1.048839   1.275902     0.82   0.411    -1.451883     3.54956
          CIV  |   5.723589   .6024077     9.50   0.000     4.542891    6.904286
          COD  |   5.604707   .9068736     6.18   0.000     3.827267    7.382146
          COL  |   2.023834   .8544073     2.37   0.018     .3492268    3.698442
          CRI  |   2.430893   .6916515     3.51   0.000     1.075281    3.786505
          DOM  |   .9673736   .6815756     1.42   0.156      -.36849    2.303237
          ECU  |  -.0749502   .6937147    -0.11   0.914    -1.434606    1.284706
          FRA  |   2.820421    1.02853     2.74   0.006     .8045382    4.836303
          GEO  |   .9114332   .6140723     1.48   0.138    -.2921265    2.114993
          HND  |   .5878629   .6973535     0.84   0.399    -.7789247    1.954651
          HRV  |   1.092669   .7005211     1.56   0.119    -.2803266    2.465666
          IDN  |   1.999196   .7538432     2.65   0.008     .5216904    3.476702
          IND  |   .7163722   .8199081     0.87   0.382    -.8906181    2.323363
          IRL  |   7.611025   1.298261     5.86   0.000     5.066481    10.15557
          IRN  |   6.914617   .6723517    10.28   0.000     5.596832    8.232403
          ISR  |   .8550626   .9900443     0.86   0.388    -1.085389    2.795514
          ITA  |   1.012212   .9231138     1.10   0.273    -.7970574    2.821482
          KEN  |   .4209482    .761286     0.55   0.580    -1.071145    1.913041
          LBN  |    7.20859    .641777    11.23   0.000      5.95073    8.466449
          LTU  |   1.052934    .715415     1.47   0.141    -.3492538    2.455122
          MDA  |   1.886619   .5569309     3.39   0.001     .7950543    2.978183
          MLI  |  -.0989383   .8777289    -0.11   0.910    -1.819255    1.621379
          MRT  |  -5.417163   .5817032    -9.31   0.000     -6.55728   -4.277046
          MWI  |   .2862354   1.042432     0.27   0.784    -1.756894    2.329365
          NGA  |   .9041324   .7892535     1.15   0.252    -.6427761    2.451041
          NOR  |   1.607051   1.202923     1.34   0.182     -.750635    3.964737
          NPL  |  -.4622591    1.08061    -0.43   0.669    -2.580215    1.655697
          PAK  |    .839764    1.02645     0.82   0.413    -1.172042     2.85157
          PAN  |   1.275369   .6933695     1.84   0.066    -.0836104    2.634348
          PHL  |   2.111353   .7023822     3.01   0.003     .7347094    3.487997
          PRY  |   .3961805   .8144676     0.49   0.627    -1.200147    1.992508
          RUS  |   .4929595   .7924551     0.62   0.534    -1.060224    2.046143
          SDN  |   6.518867   .9213811     7.08   0.000     4.712993    8.324741
          SLE  |   1.064693   1.068533     1.00   0.319    -1.029593     3.15898
          THA  |   .8979765   .6655492     1.35   0.177    -.4064759    2.202429
          TUN  |   .1934704   .4690654     0.41   0.680    -.7258808    1.112822
          UKR  |   .8628715    .633379     1.36   0.173    -.3785286    2.104272
          USA  |   2.239708   1.134427     1.97   0.048     .0162726    4.463143
          VEN  |   1.153089   .6902185     1.67   0.095    -.1997142    2.505893
               |
          year |
         1998  |   .9319622   .4395609     2.12   0.034     .0704386    1.793486
         1999  |   .2118308   .4515226     0.47   0.639    -.6731372    1.096799
         2000  |   .0074534   .4502042     0.02   0.987    -.8749306    .8898373
         2001  |   .9339816   .4074378     2.29   0.022     .1354181    1.732545
         2002  |    .548554   .4152556     1.32   0.187    -.2653321     1.36244
         2003  |   1.498807   .4048277     3.70   0.000     .7053596    2.292255
         2004  |   1.098364   .3978009     2.76   0.006     .3186886     1.87804
         2005  |   1.327179   .3987907     3.33   0.001     .5455639    2.108795
         2006  |   1.389461   .4736397     2.93   0.003     .4611441    2.317777
         2007  |   1.027442   .4218339     2.44   0.015     .2006625    1.854221
         2008  |   1.451615   .4825375     3.01   0.003     .5058592    2.397371
         2009  |   1.178912   .5660777     2.08   0.037     .0694195    2.288403
         2010  |   1.303029   .5132654     2.54   0.011     .2970475    2.309011
         2011  |   1.858978   .5259738     3.53   0.000     .8280886    2.889868
         2012  |   1.552379   .5766442     2.69   0.007     .4221774    2.682581
         2013  |   1.907712   .5138577     3.71   0.000      .900569    2.914854
         2014  |    2.09174   .5277566     3.96   0.000     1.057356    3.126124
               |
         _cons |  -1.159321   2.713975    -0.43   0.669    -6.478614    4.159973
---------------+----------------------------------------------------------------
       /athrho |   -.195973   .0896909    -2.18   0.029     -.371764    -.020182
      /lnsigma |  -2.480739   .1209402   -20.51   0.000    -2.717778   -2.243701
---------------+----------------------------------------------------------------
           rho |  -.1935021   .0863326                     -.3555337   -.0201793
         sigma |   .0836813   .0101204                      .0660213    .1060652
        lambda |  -.0161925   .0083198                     -.0324991    .0001141
--------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 4.77       Prob > chi2 = 0.0289

.         est store sanc_dur_heckman_1 

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.1935021   .0863326    -2.24   0.025    -.3627109   -.0242933
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_1
(results sanc_dur_heckman_1 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.19350212

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .08633262

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .02500304

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  4.7741424

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .02889023

. 
.   * Model (2)   
.     heckman lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust) 
note: sanc_postdur omitted because of collinearity.

Iteration 0:  Log pseudolikelihood =  150.20364  
Iteration 1:  Log pseudolikelihood =   150.9759  
Iteration 2:  Log pseudolikelihood =  152.14663  
Iteration 3:  Log pseudolikelihood =   152.1578  
Iteration 4:  Log pseudolikelihood =  152.15781  

Heckman selection model                         Number of obs     =        738
(regression model with sample selection)              Selected    =        302
                                                      Nonselected =        436

                                                Wald chi2(64)     =          .
Log pseudolikelihood =  152.1578                Prob > chi2       =          .

---------------------------------------------------------------------------------
                |               Robust
                | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
lerner          |
       sanc_dur |   .0081117   .0063537     1.28   0.202    -.0043414    .0205648
   sanc_postdur |          0  (omitted)
      sanc_type |    .009226   .0115732     0.80   0.425     -.013457     .031909
     sanc_state |  -.0024025   .0054079    -0.44   0.657    -.0130018    .0081968
       sanc_org |  -.0177615   .0384673    -0.46   0.644    -.0931561    .0576331
econ_change_gdp |    .000507   .0015793     0.32   0.748    -.0025883    .0036024
       econ_fin |  -.0342986   .0213356    -1.61   0.108    -.0761155    .0075184
 econ_asset_gdp |   -.001144   .0014188    -0.81   0.420    -.0039247    .0016368
                |
       iso3_num |
           ARG  |  -.3602773   .1452849    -2.48   0.013    -.6450304   -.0755242
           AZE  |    .025594   .1262839     0.20   0.839     -.221918     .273106
           BEN  |  -.1477618   .0788656    -1.87   0.061    -.3023355    .0068119
           BGR  |  -.0197166   .1110612    -0.18   0.859    -.2373925    .1979594
           BLR  |  -.1739663   .0857368    -2.03   0.042    -.3420074   -.0059252
           BOL  |  -.0584938   .1120805    -0.52   0.602    -.2781676      .16118
           BRA  |  -.1161533   .1083972    -1.07   0.284    -.3286078    .0963013
           CAN  |  -.1047296   .1767565    -0.59   0.554    -.4511659    .2417068
           CHE  |   -.161917   .2292926    -0.71   0.480    -.6113223    .2874883
           CIV  |   -.175475     .07834    -2.24   0.025    -.3290186   -.0219314
           COD  |  -.3582925   .0490459    -7.31   0.000    -.4544207   -.2621643
           COL  |  -.1098834   .0666935    -1.65   0.099    -.2406002    .0208334
           CRI  |  -.2425514   .0773763    -3.13   0.002    -.3942061   -.0908966
           DOM  |  -.1627715   .1107974    -1.47   0.142    -.3799304    .0543873
           ECU  |  -.1111855   .1199794    -0.93   0.354    -.3463408    .1239699
           FRA  |   -.241754   .1361676    -1.78   0.076    -.5086376    .0251296
           GEO  |  -.1374707   .0865026    -1.59   0.112    -.3070127    .0320713
           HND  |  -.0948018   .0943065    -1.01   0.315    -.2796391    .0900356
           HRV  |  -.1021505   .1001182    -1.02   0.308    -.2983786    .0940776
           IDN  |   -.192934     .07929    -2.43   0.015    -.3483395   -.0375285
           IND  |  -.2059219    .085472    -2.41   0.016     -.373444   -.0383998
           IRL  |  -.0534674    .198301    -0.27   0.787    -.4421302    .3351954
           IRN  |  -.3897275   .0669312    -5.82   0.000    -.5209103   -.2585448
           ISR  |  -.0736146   .1388455    -0.53   0.596    -.3457468    .1985177
           ITA  |   -.094299   .1121057    -0.84   0.400    -.3140222    .1254241
           KEN  |   .0263087   .0990403     0.27   0.791    -.1678068    .2204241
           LBN  |  -.0932941   .2060833    -0.45   0.651    -.4972099    .3106217
           LTU  |  -.0052454   .1233453    -0.04   0.966    -.2469978     .236507
           MDA  |  -.0479797   .0950551    -0.50   0.614    -.2342842    .1383249
           MLI  |  -.1874098   .0994629    -1.88   0.060    -.3823535     .007534
           MWI  |  -.1219088   .0521356    -2.34   0.019    -.2240927   -.0197248
           NGA  |  -.1796223   .0660861    -2.72   0.007    -.3091487   -.0500958
           NOR  |   .1416094   .1688623     0.84   0.402    -.1893547    .4725734
           NPL  |  -.0905939   .0829266    -1.09   0.275    -.2531272    .0719393
           PAK  |  -.2477894    .060915    -4.07   0.000    -.3671805   -.1283982
           PAN  |    .009512   .1295756     0.07   0.941    -.2444515    .2634756
           PHL  |  -.1777826   .0732924    -2.43   0.015    -.3214331   -.0341322
           PRY  |  -.1380361   .0993841    -1.39   0.165    -.3328253    .0567531
           RUS  |    -.25627   .1131118    -2.27   0.023     -.477965   -.0345751
           SDN  |  -.1713733   .0445289    -3.85   0.000    -.2586484   -.0840982
           SLE  |  -.0677888   .0675762    -1.00   0.316    -.2002358    .0646582
           THA  |   .0428845    .141516     0.30   0.762    -.2344819    .3202508
           TUN  |   .0980132   .1325154     0.74   0.460    -.1617123    .3577386
           UKR  |  -.0280361   .1150748    -0.24   0.808    -.2535787    .1975064
           USA  |  -.1061708   .0878312    -1.21   0.227    -.2783167    .0659751
           VEN  |  -.1008906    .086092    -1.17   0.241    -.2696279    .0678467
                |
           year |
          1998  |    .048113   .0638263     0.75   0.451    -.0769843    .1732103
          1999  |    .014683   .0991421     0.15   0.882     -.179632     .208998
          2000  |   .1064988    .075989     1.40   0.161    -.0424369    .2554346
          2001  |   .0834473   .0607279     1.37   0.169    -.0355772    .2024717
          2002  |   .1292037   .0568975     2.27   0.023     .0176867    .2407208
          2003  |   .0810528   .0646886     1.25   0.210    -.0457346    .2078401
          2004  |    .127079   .0613458     2.07   0.038     .0068434    .2473145
          2005  |   .1087086   .0645654     1.68   0.092    -.0178372    .2352545
          2006  |   .0925428     .06884     1.34   0.179    -.0423812    .2274668
          2007  |    .066657   .0773119     0.86   0.389    -.0848716    .2181855
          2008  |   .0579569   .0795914     0.73   0.467    -.0980394    .2139532
          2009  |   .0667396   .0834436     0.80   0.424    -.0968068     .230286
          2010  |   .0546035   .0884693     0.62   0.537    -.1187931    .2280001
          2011  |   .0728608   .0923185     0.79   0.430    -.1080801    .2538016
          2012  |   .0698286   .0969414     0.72   0.471    -.1201731    .2598302
          2013  |   .0702583    .103226     0.68   0.496    -.1320609    .2725774
          2014  |   .0711651   .1096676     0.65   0.516    -.1437794    .2861096
                |
          _cons |     .33178   .0863072     3.84   0.000     .1626211     .500939
----------------+----------------------------------------------------------------
sanc_dur_dummy  |
 sanc_dur_dummy |
            L1. |   2.170948   .1815684    11.96   0.000     1.815081    2.526816
                |
 pol_polity2dem |  -.9495137   .2737386    -3.47   0.001    -1.486031    -.412996
    econ_lgdppc |  -.1579534   .3140106    -0.50   0.615    -.7734029    .4574961
   econ_fdi_gdp |  -.7083795   2.137468    -0.33   0.740    -4.897739     3.48098
 econ_trade_gdp |   -.221413   .6919165    -0.32   0.749    -1.577544    1.134718
                |
       iso3_num |
           ARG  |   .8418159   .8149163     1.03   0.302    -.7553907    2.439023
           AZE  |  -.2974446   .6610372    -0.45   0.653    -1.593054    .9981646
           BEN  |   1.176089   .9293539     1.27   0.206    -.6454107     2.99759
           BGR  |   .8000769   .7382386     1.08   0.278    -.6468441    2.246998
           BLR  |   5.801865   .4916484    11.80   0.000     4.838251    6.765478
           BOL  |   .8354559   .6765963     1.23   0.217    -.4906485     2.16156
           BRA  |   .5366417   .9586417     0.56   0.576    -1.342261    2.415545
           CAN  |   2.221626   1.065954     2.08   0.037     .1323953    4.310856
           CHE  |   1.052931   1.276473     0.82   0.409    -1.448911    3.554772
           CIV  |   5.634397   .6022516     9.36   0.000     4.454006    6.814788
           COD  |   5.509734   .9057089     6.08   0.000     3.734577     7.28489
           COL  |   2.017153   .8530085     2.36   0.018     .3452869    3.689019
           CRI  |   2.417286   .6888487     3.51   0.000     1.067168    3.767405
           DOM  |   .9666367   .6817854     1.42   0.156    -.3696381    2.302911
           ECU  |  -.0709738   .6936732    -0.10   0.919    -1.430548    1.288601
           FRA  |   2.821372   1.025809     2.75   0.006     .8108228    4.831922
           GEO  |   .9129331   .6152103     1.48   0.138    -.2928569    2.118723
           HND  |    .582934   .6975983     0.84   0.403    -.7843335    1.950201
           HRV  |   1.092916   .7006887     1.56   0.119    -.2804083    2.466241
           IDN  |    2.01132   .7580605     2.65   0.008      .525549    3.497092
           IND  |   .7109872   .8209859     0.87   0.386    -.8981155     2.32009
           IRL  |   7.447199   1.302168     5.72   0.000     4.894997    9.999401
           IRN  |   6.848011   .6713451    10.20   0.000     5.532198    8.163823
           ISR  |   .8578149   .9901114     0.87   0.386    -1.082768    2.798397
           ITA  |   1.016951   .9236989     1.10   0.271    -.7934657    2.827368
           KEN  |   .4172138   .7620416     0.55   0.584     -1.07636    1.910788
           LBN  |   7.176064   .6373307    11.26   0.000     5.926918    8.425209
           LTU  |   1.053074   .7155897     1.47   0.141    -.3494557    2.455605
           MDA  |    1.87987   .5557144     3.38   0.001     .7906899     2.96905
           MLI  |  -.1008167   .8783877    -0.11   0.909    -1.822425    1.620792
           MRT  |  -5.417686   .5818877    -9.31   0.000    -6.558165   -4.277207
           MWI  |    .281547   1.042793     0.27   0.787     -1.76229    2.325384
           NGA  |   .9108987   .7905737     1.15   0.249    -.6385973    2.460395
           NOR  |    1.61505   1.203776     1.34   0.180    -.7443082    3.974408
           NPL  |  -.4646555   1.081341    -0.43   0.667    -2.584045    1.654734
           PAK  |   .8410414   1.025885     0.82   0.412    -1.169657     2.85174
           PAN  |   1.273976   .6934225     1.84   0.066    -.0851067     2.63306
           PHL  |   2.108767   .7025283     3.00   0.003     .7318369    3.485697
           PRY  |   .3943572   .8152341     0.48   0.629    -1.203472    1.992187
           RUS  |   .5057804   .7915989     0.64   0.523    -1.045725    2.057286
           SDN  |   6.453288   .9240704     6.98   0.000     4.642144    8.264433
           SLE  |   1.056304   1.067355     0.99   0.322    -1.035672    3.148281
           THA  |   .9023361   .6659787     1.35   0.175    -.4029581     2.20763
           TUN  |    .195714   .4692784     0.42   0.677    -.7240548    1.115483
           UKR  |   .8603315   .6333214     1.36   0.174    -.3809557    2.101619
           USA  |   2.244215   1.132694     1.98   0.048     .0241759    4.464253
           VEN  |   1.147226   .6896248     1.66   0.096    -.2044143    2.498865
                |
           year |
          1998  |   .9268497    .440718     2.10   0.035     .0630583    1.790641
          1999  |   .2104523   .4525884     0.46   0.642    -.6766047    1.097509
          2000  |   .0042117   .4508295     0.01   0.993    -.8793978    .8878213
          2001  |   .9289199   .4083909     2.27   0.023     .1284885    1.729351
          2002  |   .5447245   .4157994     1.31   0.190    -.2702272    1.359676
          2003  |   1.494911   .4059588     3.68   0.000     .6992469    2.290576
          2004  |   1.094713   .3990561     2.74   0.006     .3125771    1.876848
          2005  |   1.325129   .3992886     3.32   0.001     .5425377     2.10772
          2006  |   1.382086   .4748953     2.91   0.004     .4513081    2.312863
          2007  |   1.023426   .4222814     2.42   0.015       .19577    1.851082
          2008  |   1.449293   .4830235     3.00   0.003     .5025839    2.396001
          2009  |   1.178532   .5660424     2.08   0.037     .0691094    2.287955
          2010  |   1.300336   .5135094     2.53   0.011     .2938757    2.306796
          2011  |   1.853916   .5258747     3.53   0.000     .8232201    2.884611
          2012  |   1.550992   .5763547     2.69   0.007     .4213578    2.680627
          2013  |   1.906241   .5139588     3.71   0.000     .8989004    2.913582
          2014  |   2.095099   .5272238     3.97   0.000     1.061759    3.128439
                |
          _cons |  -1.143104   2.718765    -0.42   0.674    -6.471785    4.185577
----------------+----------------------------------------------------------------
        /athrho |  -.1947832   .0904656    -2.15   0.031    -.3720925    -.017474
       /lnsigma |  -2.491002   .1178529   -21.14   0.000    -2.721989   -2.260014
----------------+----------------------------------------------------------------
            rho |  -.1923567   .0871182                     -.3558207   -.0174722
          sigma |   .0828269   .0097614                      .0657438     .104349
         lambda |  -.0159323   .0082638                     -.0321291    .0002644
---------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 4.64       Prob > chi2 = 0.0313

.         est store sanc_dur_heckman_2

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.1923567   .0871182    -2.21   0.027    -.3631053   -.0216081
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_2
(results sanc_dur_heckman_2 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.19235668

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .08711825

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .0272446

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  4.6359266

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .03130923

.         
.   * Model (3)   
.     heckman lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                  

Iteration 0:  Log pseudolikelihood =  167.05966  
Iteration 1:  Log pseudolikelihood =   168.8565  
Iteration 2:  Log pseudolikelihood =   169.1381  
Iteration 3:  Log pseudolikelihood =  169.13858  
Iteration 4:  Log pseudolikelihood =  169.13858  

Heckman selection model                         Number of obs     =        738
(regression model with sample selection)              Selected    =        302
                                                      Nonselected =        436

                                                Wald chi2(64)     =          .
Log pseudolikelihood =  169.1386                Prob > chi2       =          .

----------------------------------------------------------------------------------
                 |               Robust
                 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
lerner           |
       sanc_fdur |   .0081726   .0038851     2.10   0.035      .000558    .0157872
   sanc_fpostdur |  -.0247353   .0088549    -2.79   0.005    -.0420905   -.0073801
    sanc_nonfdur |  -.0035457   .0043935    -0.81   0.420    -.0121569    .0050654
sanc_nonfpostdur |   .0039813   .0061262     0.65   0.516    -.0080258    .0159884
       sanc_type |  -.0114593   .0106821    -1.07   0.283    -.0323958    .0094772
      sanc_state |   .0036896   .0048789     0.76   0.450    -.0058728    .0132519
        sanc_org |  -.0643132   .0387732    -1.66   0.097    -.1403072    .0116808
                 |
        iso3_num |
            ARG  |  -.5093267   .1624826    -3.13   0.002    -.8277867   -.1908666
            AZE  |  -.1789691   .1165675    -1.54   0.125    -.4074372     .049499
            BEN  |   -.256081   .0773792    -3.31   0.001    -.4077414   -.1044205
            BGR  |  -.1959762   .0710467    -2.76   0.006    -.3352252   -.0567272
            BLR  |  -.3248362   .0779167    -4.17   0.000    -.4775502   -.1721222
            BOL  |  -.2738375   .1044391    -2.62   0.009    -.4785345   -.0691406
            BRA  |  -.2575243   .0744203    -3.46   0.001    -.4033854   -.1116632
            CAN  |  -.3104904   .0804853    -3.86   0.000    -.4682386   -.1527422
            CHE  |  -.5070152   .1096292    -4.62   0.000    -.7218845   -.2921459
            CIV  |  -.3115853    .080234    -3.88   0.000     -.468841   -.1543295
            COD  |  -.3182504   .0471591    -6.75   0.000    -.4106806   -.2258202
            COL  |  -.2294054   .0602445    -3.81   0.000    -.3474824   -.1113283
            CRI  |  -.3401598   .0768678    -4.43   0.000     -.490818   -.1895016
            DOM  |    -.36272    .104035    -3.49   0.000    -.5666248   -.1588153
            ECU  |  -.3158298   .1141842    -2.77   0.006    -.5396266   -.0920329
            FRA  |  -.3831014   .0773545    -4.95   0.000    -.5347134   -.2314894
            GEO  |  -.2605916   .0892779    -2.92   0.004    -.4355729   -.0856102
            HND  |  -.2042256     .06578    -3.10   0.002     -.333152   -.0752993
            HRV  |  -.2479004   .0778347    -3.18   0.001    -.4004536   -.0953472
            IDN  |  -.2590051   .0882385    -2.94   0.003    -.4319495   -.0860607
            IND  |  -.2690848   .0650314    -4.14   0.000    -.3965441   -.1416255
            IRL  |  -.2708135   .0779059    -3.48   0.001    -.4235063   -.1181208
            IRN  |  -.4154574   .0566313    -7.34   0.000    -.5264526   -.3044622
            ISR  |  -.3324887   .1179587    -2.82   0.005    -.5636834   -.1012939
            ITA  |  -.1975624   .0505323    -3.91   0.000    -.2966039   -.0985209
            KEN  |  -.1410352   .0815446    -1.73   0.084    -.3008597    .0187892
            LBN  |   -.289481    .046732    -6.19   0.000    -.3810741    -.197888
            LTU  |  -.2210448   .1144807    -1.93   0.054    -.4454229    .0033332
            MDA  |  -.2121683   .0981088    -2.16   0.031     -.404458   -.0198785
            MLI  |  -.2742398   .0796177    -3.44   0.001    -.4302877   -.1181919
            MWI  |  -.1954709   .0539302    -3.62   0.000    -.3011721   -.0897697
            NGA  |   -.128667   .0520306    -2.47   0.013    -.2306452   -.0266889
            NOR  |  -.1186065   .1030931    -1.15   0.250    -.3206654    .0834523
            NPL  |  -.2065099   .0793641    -2.60   0.009    -.3620606   -.0509592
            PAK  |  -.3144615   .0440195    -7.14   0.000    -.4007382   -.2281848
            PAN  |  -.2446336   .1042496    -2.35   0.019     -.448959   -.0403082
            PHL  |  -.3184322   .0785371    -4.05   0.000     -.472362   -.1645024
            PRY  |  -.2653028   .0870333    -3.05   0.002    -.4358849   -.0947207
            RUS  |  -.4550395   .1065271    -4.27   0.000    -.6638288   -.2462501
            SDN  |    -.21035   .0401516    -5.24   0.000    -.2890457   -.1316544
            SLE  |    .007952   .0574115     0.14   0.890    -.1045724    .1204765
            THA  |  -.1714013   .0833013    -2.06   0.040    -.3346688   -.0081338
            TUN  |   -.157758   .1074875    -1.47   0.142    -.3684295    .0529136
            UKR  |  -.2487304   .0921759    -2.70   0.007    -.4293919   -.0680689
            USA  |  -.2441471   .0845905    -2.89   0.004    -.4099415   -.0783528
            VEN  |  -.2578283   .0858726    -3.00   0.003    -.4261355   -.0895211
                 |
            year |
           1998  |   .0487231   .0555295     0.88   0.380    -.0601128     .157559
           1999  |   .0228641   .0844337     0.27   0.787     -.142623    .1883512
           2000  |   .1445612   .0656371     2.20   0.028     .0159148    .2732075
           2001  |   .1211046   .0540767     2.24   0.025     .0151162     .227093
           2002  |   .1792511   .0535822     3.35   0.001      .074232    .2842703
           2003  |   .1453057   .0648499     2.24   0.025     .0182022    .2724093
           2004  |    .207409   .0566561     3.66   0.000     .0963651    .3184529
           2005  |   .1975981   .0552185     3.58   0.000     .0893719    .3058243
           2006  |   .1944416   .0590679     3.29   0.001     .0786706    .3102126
           2007  |   .1776035    .065443     2.71   0.007     .0493376    .3058695
           2008  |   .1697126   .0661022     2.57   0.010     .0401546    .2992705
           2009  |   .1829293   .0679147     2.69   0.007     .0498189    .3160397
           2010  |   .1940115   .0717221     2.71   0.007     .0534387    .3345844
           2011  |   .2155365   .0736997     2.92   0.003     .0710878    .3599852
           2012  |   .2235638   .0767054     2.91   0.004      .073224    .3739036
           2013  |   .2134792   .0811632     2.63   0.009     .0544023    .3725562
           2014  |   .2204788   .0844602     2.61   0.009     .0549398    .3860178
                 |
           _cons |    .379796   .0770865     4.93   0.000     .2287092    .5308828
-----------------+----------------------------------------------------------------
sanc_dur_dummy   |
  sanc_dur_dummy |
             L1. |   2.171836    .181609    11.96   0.000     1.815889    2.527783
                 |
  pol_polity2dem |  -.9509327   .2715698    -3.50   0.000      -1.4832   -.4186657
     econ_lgdppc |  -.1436289   .3161521    -0.45   0.650    -.7632756    .4760177
    econ_fdi_gdp |  -.6687802   2.138416    -0.31   0.754    -4.859998    3.522437
  econ_trade_gdp |  -.2026762   .6913004    -0.29   0.769      -1.5576    1.152248
                 |
        iso3_num |
            ARG  |   .8444877   .8143224     1.04   0.300    -.7515549     2.44053
            AZE  |   -.299045   .6616948    -0.45   0.651    -1.595943    .9978529
            BEN  |   1.205907   .9308743     1.30   0.195    -.6185729    3.030387
            BGR  |   .7885993   .7373845     1.07   0.285    -.6566479    2.233846
            BLR  |   5.532362   .4933528    11.21   0.000     4.565408    6.499316
            BOL  |   .8548866   .6764017     1.26   0.206    -.4708364     2.18061
            BRA  |    .539131   .9603501     0.56   0.575    -1.343121    2.421383
            CAN  |   2.200701   1.075421     2.05   0.041     .0929144    4.308487
            CHE  |   1.005916   1.283173     0.78   0.433    -1.509056    3.520889
            CIV  |   5.408641   .6053115     8.94   0.000     4.222252    6.595029
            COD  |   5.316345   .9161722     5.80   0.000      3.52068    7.112009
            COL  |   2.045495   .8612114     2.38   0.018     .3575514    3.733438
            CRI  |   2.411001   .6918273     3.48   0.000     1.055044    3.766957
            DOM  |   .9709666   .6817556     1.42   0.154    -.3652498    2.307183
            ECU  |   -.065997   .6933158    -0.10   0.924    -1.424871    1.292877
            FRA  |   2.788585   1.032918     2.70   0.007     .7641025    4.813066
            GEO  |   .9145112   .6139763     1.49   0.136    -.2888603    2.117883
            HND  |   .5976884   .6965059     0.86   0.391     -.767438    1.962815
            HRV  |   1.081978   .7038717     1.54   0.124    -.2975849    2.461541
            IDN  |   2.016839   .7546203     2.67   0.008     .5378108    3.495868
            IND  |   .7439661   .8208653     0.91   0.365    -.8649004    2.352833
            IRL  |   7.290807   1.309533     5.57   0.000      4.72417    9.857444
            IRN  |   6.599811   .6691169     9.86   0.000     5.288366    7.911256
            ISR  |   .8353792   .9947596     0.84   0.401    -1.114314    2.785072
            ITA  |   .9955773   .9273319     1.07   0.283    -.8219598    2.813114
            KEN  |   .4452755    .762899     0.58   0.559    -1.049979     1.94053
            LBN  |   6.922604   .6433665    10.76   0.000     5.661629     8.18358
            LTU  |   1.030468   .7184855     1.43   0.152    -.3777381    2.438673
            MDA  |   1.896202   .5570994     3.40   0.001     .8043072    2.988097
            MLI  |  -.0570954   .8797008    -0.06   0.948    -1.781277    1.667086
            MRT  |  -5.327807    .581606    -9.16   0.000    -6.467734   -4.187881
            MWI  |   .3191795   1.045774     0.31   0.760      -1.7305    2.368859
            NGA  |   .8967509   .7789723     1.15   0.250    -.6300067    2.423509
            NOR  |   1.568289   1.209673     1.30   0.195    -.8026266    3.939205
            NPL  |   -.425537   1.083638    -0.39   0.695    -2.549429    1.698355
            PAK  |   .8775691   1.023991     0.86   0.391    -1.129416    2.884554
            PAN  |   1.253101   .6945512     1.80   0.071    -.1081942    2.614397
            PHL  |   2.165875   .7084873     3.06   0.002     .7772656    3.554485
            PRY  |    .394888   .8142103     0.48   0.628    -1.200935    1.990711
            RUS  |   .5059382   .7937333     0.64   0.524     -1.04975    2.061627
            SDN  |   6.184014   .9235484     6.70   0.000     4.373893    7.994136
            SLE  |   1.083031    1.07038     1.01   0.312    -1.014875    3.180937
            THA  |   .8875002   .6644274     1.34   0.182    -.4147536    2.189754
            TUN  |   .1954745   .4691367     0.42   0.677    -.7240164    1.114966
            UKR  |   .8654992   .6329761     1.37   0.172    -.3751112     2.10611
            USA  |    2.22363   1.142555     1.95   0.052    -.0157369    4.462997
            VEN  |    1.15297   .6907736     1.67   0.095    -.2009213    2.506861
                 |
            year |
           1998  |   .9444606   .4429009     2.13   0.033     .0763908     1.81253
           1999  |   .2210337    .457682     0.48   0.629    -.6760065    1.118074
           2000  |   .0232609   .4489495     0.05   0.959    -.8566639    .9031857
           2001  |   .9497523   .4104506     2.31   0.021     .1452839    1.754221
           2002  |   .5854144   .4184406     1.40   0.162    -.2347141    1.405543
           2003  |   1.510987   .4060192     3.72   0.000     .7152041     2.30677
           2004  |   1.112311   .4010322     2.77   0.006     .3263027     1.89832
           2005  |   1.342984   .4032885     3.33   0.001     .5525531    2.133415
           2006  |   1.403597   .4784898     2.93   0.003     .4657741     2.34142
           2007  |   1.036538   .4278162     2.42   0.015     .1980342    1.875043
           2008  |   1.463281   .4879035     3.00   0.003     .5070077    2.419554
           2009  |   1.186257   .5700488     2.08   0.037     .0689814    2.303532
           2010  |   1.308576   .5166924     2.53   0.011     .2958772    2.321274
           2011  |   1.867324   .5297361     3.53   0.000     .8290602    2.905587
           2012  |   1.553847   .5799749     2.68   0.007      .417117    2.690577
           2013  |   1.915645   .5189716     3.69   0.000     .8984796    2.932811
           2014  |   2.097155   .5325781     3.94   0.000     1.053321    3.140989
                 |
           _cons |  -1.291644   2.730138    -0.47   0.636    -6.642617    4.059329
-----------------+----------------------------------------------------------------
         /athrho |  -.2178971   .0943045    -2.31   0.021    -.4027305   -.0330637
        /lnsigma |  -2.545755   .1240282   -20.53   0.000    -2.788845   -2.302664
-----------------+----------------------------------------------------------------
             rho |  -.2145129    .089965                     -.3822829   -.0330517
           sigma |   .0784138   .0097255                      .0614922    .0999921
          lambda |  -.0168208   .0079434                     -.0323895   -.0012521
----------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 5.34       Prob > chi2 = 0.0209

.         est store sanc_dur_heckman_3

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.2145129    .089965    -2.38   0.017     -.390841   -.0381847
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_3
(results sanc_dur_heckman_3 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.21451285

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .089965

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .01710684

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  5.3387324

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .02085663

.         
.   * Model (4)
.     heckman lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                   

Iteration 0:  Log pseudolikelihood =  168.99575  
Iteration 1:  Log pseudolikelihood =  169.28751  
Iteration 2:  Log pseudolikelihood =  170.96853  
Iteration 3:  Log pseudolikelihood =  170.99231  
Iteration 4:  Log pseudolikelihood =  170.99232  

Heckman selection model                         Number of obs     =        738
(regression model with sample selection)              Selected    =        302
                                                      Nonselected =        436

                                                Wald chi2(67)     =          .
Log pseudolikelihood =  170.9923                Prob > chi2       =          .

----------------------------------------------------------------------------------
                 |               Robust
                 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
lerner           |
       sanc_fdur |   .0084997   .0039178     2.17   0.030      .000821    .0161783
   sanc_fpostdur |  -.0234495   .0085678    -2.74   0.006     -.040242    -.006657
    sanc_nonfdur |  -.0034218   .0044875    -0.76   0.446    -.0122171    .0053736
sanc_nonfpostdur |   .0029944   .0061215     0.49   0.625    -.0090036    .0149924
       sanc_type |  -.0113559   .0108688    -1.04   0.296    -.0326584    .0099465
      sanc_state |   .0045423   .0050608     0.90   0.369    -.0053768    .0144613
        sanc_org |  -.0610986   .0377754    -1.62   0.106    -.1351372    .0129399
 econ_change_gdp |   .0012738   .0014232     0.90   0.371    -.0015156    .0040632
        econ_fin |  -.0189513   .0204735    -0.93   0.355    -.0590787     .021176
  econ_asset_gdp |   -.000867   .0013443    -0.64   0.519    -.0035018    .0017677
                 |
        iso3_num |
            ARG  |  -.4757413   .1441808    -3.30   0.001    -.7583306   -.1931521
            AZE  |  -.1610372   .1168624    -1.38   0.168    -.3900832    .0680088
            BEN  |  -.2470673   .0787827    -3.14   0.002    -.4014785    -.092656
            BGR  |  -.1472165   .1092917    -1.35   0.178    -.3614242    .0669912
            BLR  |  -.3032156   .0913157    -3.32   0.001    -.4821911   -.1242401
            BOL  |  -.2503898   .1066351    -2.35   0.019    -.4593908   -.0413888
            BRA  |  -.2185649   .1134608    -1.93   0.054     -.440944    .0038143
            CAN  |  -.1990897   .1764999    -1.13   0.259    -.5450232    .1468438
            CHE  |   -.369835   .2248689    -1.64   0.100    -.8105698    .0708999
            CIV  |  -.3086758   .0786524    -3.92   0.000    -.4628317     -.15452
            COD  |  -.3173313   .0504172    -6.29   0.000    -.4161472   -.2185155
            COL  |  -.2055655   .0746783    -2.75   0.006    -.3519323   -.0591986
            CRI  |  -.3090462   .0866405    -3.57   0.000    -.4788584   -.1392339
            DOM  |  -.3498739   .1042287    -3.36   0.001    -.5541584   -.1455893
            ECU  |  -.3010867   .1150024    -2.62   0.009    -.5264873    -.075686
            FRA  |  -.2930012   .1394631    -2.10   0.036    -.5663439   -.0196585
            GEO  |  -.2412389   .0891188    -2.71   0.007    -.4159086   -.0665692
            HND  |  -.1728986    .086295    -2.00   0.045    -.3420337   -.0037635
            HRV  |  -.1952412   .1049374    -1.86   0.063    -.4009148    .0104324
            IDN  |  -.2339218   .0879041    -2.66   0.008    -.4062107   -.0616328
            IND  |  -.2652651   .0879218    -3.02   0.003    -.4375886   -.0929416
            IRL  |  -.1315177   .1933519    -0.68   0.496    -.5104804    .2474451
            IRN  |  -.4149606   .0673828    -6.16   0.000    -.5470284   -.2828928
            ISR  |   -.279907   .1336582    -2.09   0.036    -.5418723   -.0179418
            ITA  |  -.1509661   .1100825    -1.37   0.170    -.3667238    .0647916
            KEN  |  -.1170513   .0892804    -1.31   0.190    -.2920377    .0579351
            LBN  |  -.1674239   .1972836    -0.85   0.396    -.5540926    .2192449
            LTU  |  -.1873116   .1181412    -1.59   0.113     -.418864    .0442408
            MDA  |  -.1853898   .0989788    -1.87   0.061    -.3793846     .008605
            MLI  |  -.2638444   .0828903    -3.18   0.001    -.4263064   -.1013825
            MWI  |  -.1830313   .0529557    -3.46   0.001    -.2868225   -.0792401
            NGA  |   -.121156   .0516609    -2.35   0.019    -.2224096   -.0199025
            NOR  |  -.0177383   .1664786    -0.11   0.915    -.3440303    .3085538
            NPL  |  -.1828042    .082429    -2.22   0.027     -.344362   -.0212465
            PAK  |  -.2938789   .0577362    -5.09   0.000    -.4070397   -.1807181
            PAN  |  -.1941259   .1255703    -1.55   0.122    -.4402393    .0519874
            PHL  |  -.2949487   .0907107    -3.25   0.001    -.4727383   -.1171591
            PRY  |   -.245237   .0903686    -2.71   0.007    -.4223561   -.0681179
            RUS  |  -.4131711   .1111513    -3.72   0.000    -.6310236   -.1953185
            SDN  |  -.2068304   .0384905    -5.37   0.000    -.2822703   -.1313905
            SLE  |   .0155073   .0571651     0.27   0.786    -.0965341    .1275488
            THA  |  -.0857183   .1422097    -0.60   0.547    -.3644443    .1930076
            TUN  |  -.1091436    .141196    -0.77   0.440    -.3858827    .1675954
            UKR  |   -.180794   .1094846    -1.65   0.099      -.39538    .0337919
            USA  |  -.1899714   .0956885    -1.99   0.047    -.3775174   -.0024254
            VEN  |  -.2347564   .0848942    -2.77   0.006     -.401146   -.0683668
                 |
            year |
           1998  |   .0552599   .0528485     1.05   0.296    -.0483212     .158841
           1999  |    .029642     .08179     0.36   0.717    -.1306636    .1899475
           2000  |   .1507123   .0632214     2.38   0.017     .0268008    .2746239
           2001  |   .1255682   .0540978     2.32   0.020     .0195385     .231598
           2002  |   .1735226   .0513255     3.38   0.001     .0729265    .2741187
           2003  |   .1366281   .0618432     2.21   0.027     .0154176    .2578385
           2004  |   .1975316   .0541293     3.65   0.000     .0914401    .3036232
           2005  |     .18821   .0532576     3.53   0.000     .0838271    .2925929
           2006  |   .1844943   .0572594     3.22   0.001      .072268    .2967206
           2007  |   .1689329   .0649206     2.60   0.009     .0416909     .296175
           2008  |   .1677626   .0657332     2.55   0.011     .0389278    .2965974
           2009  |   .1889643   .0688271     2.75   0.006     .0540657    .3238629
           2010  |   .1883998   .0710498     2.65   0.008     .0491447    .3276549
           2011  |   .2132972    .073786     2.89   0.004     .0686794    .3579151
           2012  |   .2175626   .0767811     2.83   0.005     .0670744    .3680508
           2013  |   .2083841   .0818753     2.55   0.011     .0479114    .3688568
           2014  |   .2186192   .0865742     2.53   0.012     .0489368    .3883016
                 |
           _cons |   .3860891   .0817662     4.72   0.000     .2258303    .5463478
-----------------+----------------------------------------------------------------
sanc_dur_dummy   |
  sanc_dur_dummy |
             L1. |   2.171062   .1815769    11.96   0.000     1.815178    2.526946
                 |
  pol_polity2dem |  -.9483024   .2724607    -3.48   0.001    -1.482316   -.4142892
     econ_lgdppc |  -.1495834   .3164102    -0.47   0.636     -.769736    .4705692
    econ_fdi_gdp |  -.6755313   2.138163    -0.32   0.752    -4.866253    3.515191
  econ_trade_gdp |   -.203008   .6918688    -0.29   0.769    -1.559046     1.15303
                 |
        iso3_num |
            ARG  |   .8493123   .8156044     1.04   0.298    -.7492429    2.447868
            AZE  |  -.2962953   .6616241    -0.45   0.654    -1.593055    1.000464
            BEN  |   1.200303   .9316095     1.29   0.198    -.6256182    3.026224
            BGR  |   .7913451    .737835     1.07   0.283    -.6547851    2.237475
            BLR  |   5.871107   .4947618    11.87   0.000     4.901392    6.840823
            BOL  |   .8497975   .6769397     1.26   0.209      -.47698    2.176575
            BRA  |   .5433543   .9609413     0.57   0.572    -1.340056    2.426765
            CAN  |   2.212878   1.074833     2.06   0.040     .1062437    4.319513
            CHE  |   1.023411   1.283934     0.80   0.425    -1.493053    3.539876
            CIV  |    5.72276   .6078261     9.42   0.000     4.531443    6.914077
            COD  |   5.596766   .9150084     6.12   0.000     3.803383     7.39015
            COL  |    2.04408   .8604334     2.38   0.018     .3576619    3.730499
            CRI  |   2.403988   .6893164     3.49   0.000     1.052953    3.755023
            DOM  |   .9732498   .6820746     1.43   0.154    -.3635918    2.310092
            ECU  |  -.0631739   .6936566    -0.09   0.927    -1.422716    1.296368
            FRA  |   2.799761   1.030718     2.72   0.007     .7795917    4.819931
            GEO  |   .9147071   .6143636     1.49   0.137    -.2894235    2.118838
            HND  |   .5925772   .6972062     0.85   0.395    -.7739218    1.959076
            HRV  |   1.088379   .7039521     1.55   0.122    -.2913414      2.4681
            IDN  |   2.023272   .7581808     2.67   0.008     .5372648    3.509279
            IND  |   .7345155   .8221599     0.89   0.372    -.8768883    2.345919
            IRL  |   7.482493   1.314064     5.69   0.000     4.906975    10.05801
            IRN  |    6.92202   .6693298    10.34   0.000     5.610158    8.233882
            ISR  |   .8468947   .9946641     0.85   0.395    -1.102611    2.796401
            ITA  |    1.00898    .927668     1.09   0.277    -.8092159    2.827176
            KEN  |   .4369937    .763745     0.57   0.567    -1.059919    1.933907
            LBN  |     7.2997   .6411549    11.39   0.000     6.043059     8.55634
            LTU  |   1.038297   .7188083     1.44   0.149    -.3705415    2.447135
            MDA  |   1.888924    .555945     3.40   0.001     .7992914    2.978556
            MLI  |  -.0657382   .8806023    -0.07   0.940    -1.791687    1.660211
            MRT  |  -5.413153   .5816058    -9.31   0.000    -6.553079   -4.273227
            MWI  |     .30992   1.046253     0.30   0.767    -1.740698    2.360538
            NGA  |   .8977607   .7807341     1.15   0.250    -.6324499    2.427971
            NOR  |   1.589393   1.210627     1.31   0.189    -.7833922    3.962178
            NPL  |  -.4354461   1.084123    -0.40   0.688    -2.560289    1.689397
            PAK  |   .8723234   1.024609     0.85   0.395    -1.135874    2.880521
            PAN  |   1.258084   .6948121     1.81   0.070    -.1037223    2.619891
            PHL  |   2.165116   .7085496     3.06   0.002     .7763841    3.553847
            PRY  |   .3959472   .8150673     0.49   0.627    -1.201555     1.99345
            RUS  |   .5168562    .793199     0.65   0.515    -1.037785    2.071498
            SDN  |   6.553288   .9266166     7.07   0.000     4.737153    8.369423
            SLE  |   1.071684   1.070102     1.00   0.317    -1.025678    3.169046
            THA  |   .8935917    .665095     1.34   0.179    -.4099705    2.197154
            TUN  |   .1992272   .4693818     0.42   0.671    -.7207441    1.119199
            UKR  |   .8637628   .6329486     1.36   0.172    -.3767937    2.104319
            USA  |   2.238379   1.141623     1.96   0.050     .0008392    4.475918
            VEN  |    1.15055   .6903522     1.67   0.096    -.2025154    2.503615
                 |
            year |
           1998  |   .9358893   .4441246     2.11   0.035     .0654211    1.806358
           1999  |   .2147197   .4584065     0.47   0.639    -.6837406     1.11318
           2000  |   .0167436   .4496291     0.04   0.970    -.8645133    .8980004
           2001  |   .9409001   .4113657     2.29   0.022     .1346382    1.747162
           2002  |   .5779383   .4189914     1.38   0.168    -.2432698    1.399146
           2003  |   1.503389   .4071943     3.69   0.000     .7053031    2.301475
           2004  |   1.105685   .4018625     2.75   0.006     .3180494    1.893321
           2005  |    1.33809     .40341     3.32   0.001     .5474211    2.128759
           2006  |   1.391546   .4793356     2.90   0.004     .4520652    2.331026
           2007  |   1.031311   .4276221     2.41   0.016     .1931867    1.869435
           2008  |   1.459593   .4876942     2.99   0.003     .5037297    2.415456
           2009  |   1.186973   .5694764     2.08   0.037     .0708203    2.303127
           2010  |   1.306564   .5163251     2.53   0.011     .2945852    2.318543
           2011  |   1.863897   .5293512     3.52   0.000     .8263874    2.901406
           2012  |   1.554194   .5792224     2.68   0.007     .4189393     2.68945
           2013  |   1.914693   .5184438     3.69   0.000     .8985619    2.930824
           2014  |   2.100836   .5316762     3.95   0.000      1.05877    3.142902
                 |
           _cons |  -1.241626   2.735828    -0.45   0.650     -6.60375    4.120497
-----------------+----------------------------------------------------------------
         /athrho |  -.2148782    .094138    -2.28   0.022    -.3993853   -.0303712
        /lnsigma |  -2.552143   .1224784   -20.84   0.000    -2.792196    -2.31209
-----------------+----------------------------------------------------------------
             rho |   -.211631   .0899218                     -.3794229   -.0303618
           sigma |   .0779145   .0095428                      .0612865     .099054
          lambda |  -.0164891   .0078846                     -.0319427   -.0010356
----------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 5.21       Prob > chi2 = 0.0225

.         est store sanc_dur_heckman_4

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |   -.211631   .0899218    -2.35   0.019    -.3878745   -.0353876
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_4
(results sanc_dur_heckman_4 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.21163104

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .08992178

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .01859753

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  5.2102068

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .02245466

.         
.   * Export regression table             
.     esttab sanc_dur_heckman_1 sanc_dur_heckman_2 ///
>                sanc_dur_heckman_3 sanc_dur_heckman_4 ///
>                using "[Appendix 7] sanc_dur_heckman.rtf", ///
>                b(3) se(3) ///
>                scalars(N N_selected Rho Rho_SE Rho_p WaldChi2 Wald_p ll) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp ///
>                                  L.sanc_dur_dummy ///                            
>                  pol_polity2dem econ_lgdppc ///
>                                  econ_fdi_gdp econ_trade_gdp _cons ) ///
>                    keep(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp ///
>                                  L.sanc_dur_dummy ///                            
>                  pol_polity2dem econ_lgdppc ///
>                                  econ_fdi_gdp econ_trade_gdp _cons ) ///
>                          replace
(file [Appendix 7] sanc_dur_heckman.rtf not found)
(output written to [Appendix 7] sanc_dur_heckman.rtf)

. 
.         
. ** Appx. 8. Pre-1996: Excluded / Sample: 1-Year Lag / IV: Duration / OLS *******
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Model (1)                   
.         reghdfe lerner l.sanc_dur l.sanc_postdur ///
>              l.sanc_type l.sanc_state l.sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   5,   1729) =       7.44
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5453
                                                  Adj R-squared   =     0.5090
                                                  Within R-sq.    =     0.0094
                                                  Root MSE        =     0.1007

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    sanc_dur |
         L1. |  -.0012104   .0018187    -0.67   0.506    -.0047774    .0023566
             |
sanc_postdur |
         L1. |  -.0056751    .001796    -3.16   0.002    -.0091976   -.0021525
             |
   sanc_type |
         L1. |   .0066837   .0051369     1.30   0.193    -.0033914    .0167588
             |
  sanc_state |
         L1. |  -.0074901   .0016471    -4.55   0.000    -.0107207   -.0042596
             |
    sanc_org |
         L1. |   .0051449    .012173     0.42   0.673    -.0187305    .0290202
             |
       _cons |   .2623781   .0030631    85.66   0.000     .2563703    .2683858
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.         est store lag_dur_ols_1 

.         
.   * Model (2)                   
.         reghdfe lerner l.sanc_dur l.sanc_postdur ///
>              l.sanc_type l.sanc_state l.sanc_org ///
>                  l.econ_change_gdp l.econ_fin l.econ_asset, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   8,   1726) =       6.02
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5523
                                                  Adj R-squared   =     0.5157
                                                  Within R-sq.    =     0.0246
                                                  Root MSE        =     0.1000

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
       sanc_dur |
            L1. |  -.0015492   .0017951    -0.86   0.388    -.0050699    .0019715
                |
   sanc_postdur |
            L1. |  -.0056015   .0017424    -3.21   0.001    -.0090188   -.0021841
                |
      sanc_type |
            L1. |   .0077953   .0051875     1.50   0.133    -.0023791    .0179697
                |
     sanc_state |
            L1. |  -.0078267   .0016927    -4.62   0.000    -.0111466   -.0045068
                |
       sanc_org |
            L1. |   .0051723   .0123957     0.42   0.677    -.0191399    .0294844
                |
econ_change_gdp |
            L1. |   .0030809   .0010784     2.86   0.004     .0009657    .0051961
                |
       econ_fin |
            L1. |   .0056235   .0069546     0.81   0.419    -.0080168    .0192639
                |
 econ_asset_gdp |
            L1. |  -.0003411   .0001486    -2.30   0.022    -.0006325   -.0000497
                |
          _cons |    .269778   .0100695    26.79   0.000     .2500283    .2895278
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.         est store lag_dur_ols_2 

.         
.   * Model (3)                           
.         reghdfe lerner l.sanc_fdur l.sanc_fpostdur ///
>              l.sanc_nonfdur l.sanc_nonfpostdur  ///
>              l.sanc_type l.sanc_state l.sanc_org, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   7,   1727) =       8.53
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5492
                                                  Adj R-squared   =     0.5127
                                                  Within R-sq.    =     0.0179
                                                  Root MSE        =     0.1003

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |
             L1. |   .0053001   .0024721     2.14   0.032     .0004515    .0101487
                 |
   sanc_fpostdur |
             L1. |  -.0110627   .0026333    -4.20   0.000    -.0162276   -.0058978
                 |
    sanc_nonfdur |
             L1. |   -.001685   .0020089    -0.84   0.402    -.0056251    .0022552
                 |
sanc_nonfpostdur |
             L1. |  -.0019304   .0020819    -0.93   0.354    -.0060138     .002153
                 |
       sanc_type |
             L1. |   .0018798   .0047522     0.40   0.692    -.0074408    .0112004
                 |
      sanc_state |
             L1. |  -.0068537    .001609    -4.26   0.000    -.0100094    -.003698
                 |
        sanc_org |
             L1. |  -.0082354   .0123801    -0.67   0.506    -.0325171    .0160462
                 |
           _cons |    .264349   .0031012    85.24   0.000     .2582666    .2704314
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.     est store lag_dur_ols_3 

.         
.   * Model (4)                   
.         reghdfe lerner l.sanc_fdur l.sanc_fpostdur ///
>              l.sanc_nonfdur l.sanc_nonfpostdur  ///
>              l.sanc_type l.sanc_state l.sanc_org ///
>                  l.econ_change_gdp l.econ_fin l.econ_asset, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(  10,   1724) =       7.14
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5563
                                                  Adj R-squared   =     0.5195
                                                  Within R-sq.    =     0.0332
                                                  Root MSE        =     0.0996

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |
             L1. |   .0044878   .0025679     1.75   0.081    -.0005487    .0095244
                 |
   sanc_fpostdur |
             L1. |  -.0116355   .0027739    -4.19   0.000    -.0170761    -.006195
                 |
    sanc_nonfdur |
             L1. |  -.0017141   .0019575    -0.88   0.381    -.0055535    .0021253
                 |
sanc_nonfpostdur |
             L1. |   -.001846   .0020384    -0.91   0.365    -.0058441     .002152
                 |
       sanc_type |
             L1. |   .0030457   .0048134     0.63   0.527     -.006395    .0124864
                 |
      sanc_state |
             L1. |  -.0071627   .0016551    -4.33   0.000    -.0104089   -.0039164
                 |
        sanc_org |
             L1. |  -.0080934    .012603    -0.64   0.521    -.0328121    .0166254
                 |
 econ_change_gdp |
             L1. |    .003138   .0010682     2.94   0.003      .001043     .005233
                 |
        econ_fin |
             L1. |   .0047645   .0069841     0.68   0.495    -.0089336    .0184627
                 |
  econ_asset_gdp |
             L1. |  -.0003281   .0001499    -2.19   0.029     -.000622   -.0000342
                 |
           _cons |   .2710058    .010053    26.96   0.000     .2512885    .2907231
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.     est store lag_dur_ols_4             

.         
.   * Export regression table             
.     esttab lag_dur_ols_1  lag_dur_ols_2 ///
>                lag_dur_ols_3  lag_dur_ols_4 ///
>                using "[Appendix 8] lag_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(L.sanc_dur  L.sanc_postdur ///
>                          L.sanc_fdur L.sanc_fpostdur ///
>                                  L.sanc_nonfdur L.sanc_nonfpostdur ///             
>                      L.sanc_type L.sanc_state L.sanc_org ///
>                          L.econ_change_gdp L.econ_fin L.econ_asset_gdp) replace         
(file [Appendix 8] lag_dur_ols.rtf not found)
(output written to [Appendix 8] lag_dur_ols.rtf)

. 
. 
. ** Appx. 9. Pre-1996: Excluded / Sample: Diff: 5-7 Yr / IV: Duration / OLS *****
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 4) by(iso3_num)   

.           
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff567 = (F5.lerner + F6.lerner + F7.lerner)/3 - lerner 
(821 missing values generated)

. 
.   * Model (1)
.         reghdfe lerner_diff567 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   5,   1047) =       1.61
                                                  Prob > F        =     0.1538
                                                  R-squared       =     0.2415
                                                  Adj R-squared   =     0.1473
                                                  Within R-sq.    =     0.0078
                                                  Root MSE        =     0.1363

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0165784    .008161     2.03   0.042     .0005647    .0325922
sanc_postdur_dummy_sum |    .004315   .0052476     0.82   0.411    -.0059821    .0146121
         sanc_type_max |  -.0130365   .0097105    -1.34   0.180    -.0320907    .0060178
        sanc_state_max |   .0056301   .0030415     1.85   0.064     -.000338    .0115982
          sanc_org_max |  -.0079453   .0271973    -0.29   0.770    -.0613127    .0454221
                 _cons |   .0268468   .0065744     4.08   0.000     .0139464    .0397472
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_1

.         
.   * Model (2)    
.         reghdfe lerner_diff567 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   8,   1044) =       2.17
                                                  Prob > F        =     0.0274
                                                  R-squared       =     0.2466
                                                  Adj R-squared   =     0.1506
                                                  Within R-sq.    =     0.0146
                                                  Root MSE        =     0.1360

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0181744   .0083171     2.19   0.029     .0018543    .0344945
sanc_postdur_dummy_sum |   .0037512   .0053409     0.70   0.483    -.0067289    .0142314
         sanc_type_max |  -.0136599   .0096109    -1.42   0.156    -.0325189     .005199
        sanc_state_max |   .0053399   .0029762     1.79   0.073    -.0005001    .0111798
          sanc_org_max |   -.007917   .0273265    -0.29   0.772     -.061538    .0457041
  econ_change_gdp_mean |  -.0033181   .0032844    -1.01   0.313     -.009763    .0031268
          econ_fin_max |   .0263376   .0145818     1.81   0.071    -.0022754    .0549505
   econ_asset_gdp_mean |  -.0000655   .0003039    -0.22   0.829    -.0006619    .0005309
                 _cons |   .0330058   .0287642     1.15   0.251    -.0234364    .0894479
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_2

. 
.   * Model (3)           
.         reghdfe lerner_diff567 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   7,   1045) =       2.63
                                                  Prob > F        =     0.0106
                                                  R-squared       =     0.2445
                                                  Adj R-squared   =     0.1490
                                                  Within R-sq.    =     0.0118
                                                  Root MSE        =     0.1361

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |    .018454   .0082541     2.24   0.026     .0022574    .0346506
   sanc_fpostdur_dummy_sum |   .0028681   .0096393     0.30   0.766    -.0160466    .0217828
    sanc_nonfdur_dummy_sum |   .0173251   .0084796     2.04   0.041     .0006861    .0339641
sanc_nonfpostdur_dummy_sum |   .0040155   .0052894     0.76   0.448    -.0063634    .0143945
             sanc_type_max |  -.0200858   .0095693    -2.10   0.036     -.038863   -.0013086
            sanc_state_max |   .0062273   .0030613     2.03   0.042     .0002202    .0122344
              sanc_org_max |  -.0198266   .0308053    -0.64   0.520    -.0802739    .0406207
                     _cons |   .0258182   .0067298     3.84   0.000     .0126126    .0390237
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_3

.         
.   * Model (4)    
.         reghdfe lerner_diff567 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(  10,   1042) =       2.70
                                                  Prob > F        =     0.0029
                                                  R-squared       =     0.2502
                                                  Adj R-squared   =     0.1531
                                                  Within R-sq.    =     0.0193
                                                  Root MSE        =     0.1358

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0223337   .0086692     2.58   0.010     .0053226    .0393449
   sanc_fpostdur_dummy_sum |   .0043751   .0094438     0.46   0.643    -.0141559    .0229061
    sanc_nonfdur_dummy_sum |   .0181618   .0085933     2.11   0.035     .0012996     .035024
sanc_nonfpostdur_dummy_sum |   .0049237   .0053229     0.93   0.355    -.0055212    .0153686
             sanc_type_max |  -.0211112   .0095362    -2.21   0.027    -.0398235   -.0023988
            sanc_state_max |   .0061725   .0030304     2.04   0.042     .0002261     .012119
              sanc_org_max |  -.0239469    .031093    -0.77   0.441    -.0849588    .0370651
      econ_change_gdp_mean |  -.0035046   .0032432    -1.08   0.280    -.0098686    .0028594
              econ_fin_max |   .0272892   .0143819     1.90   0.058    -.0009316    .0555101
       econ_asset_gdp_mean |  -.0000118   .0003065    -0.04   0.969    -.0006132    .0005896
                     _cons |   .0283891    .028014     1.01   0.311    -.0265813    .0833595
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_4

.         
.   * Export regression table     
.     esttab diff567_dur_ols_1 diff567_dur_ols_2 ///
>                diff567_dur_ols_3 diff567_dur_ols_4 ///
>                using "[Appendix 9] diff567_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace         
(file [Appendix 9] diff567_dur_ols.rtf not found)
(output written to [Appendix 9] diff567_dur_ols.rtf)

.         
. 
. ** Appx. 10. Pre-1996: Excluded / Sample: Diff: 6-8 Yr / IV: Duration / OLS ****
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 5) by(iso3_num)   

. 
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff678 = (F6.lerner + F7.lerner + F8.lerner)/3 - lerner
(936 missing values generated)

. 
.   * Model (1)
.         reghdfe lerner_diff678 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   5,    933) =       2.47
                                                  Prob > F        =     0.0308
                                                  R-squared       =     0.3661
                                                  Adj R-squared   =     0.2791
                                                  Within R-sq.    =     0.0128
                                                  Root MSE        =     0.1209

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0159755    .007383     2.16   0.031     .0014863    .0304648
sanc_postdur_dummy_sum |   .0069229   .0051531     1.34   0.179    -.0031901    .0170359
         sanc_type_max |  -.0140519   .0105955    -1.33   0.185    -.0348457    .0067419
        sanc_state_max |     .00676    .003129     2.16   0.031     .0006193    .0129008
          sanc_org_max |   .0134127   .0263833     0.51   0.611    -.0383648    .0651901
                 _cons |   .0290377   .0071898     4.04   0.000     .0149278    .0431477
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_1

.         
.   * Model (2)    
.         reghdfe lerner_diff678 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   8,    930) =       2.06
                                                  Prob > F        =     0.0374
                                                  R-squared       =     0.3676
                                                  Adj R-squared   =     0.2785
                                                  Within R-sq.    =     0.0152
                                                  Root MSE        =     0.1210

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0168095   .0074126     2.27   0.024     .0022621    .0313568
sanc_postdur_dummy_sum |   .0064928   .0052292     1.24   0.215    -.0037696    .0167553
         sanc_type_max |  -.0142745   .0106023    -1.35   0.179    -.0350817    .0065328
        sanc_state_max |   .0065932   .0031165     2.12   0.035      .000477    .0127094
          sanc_org_max |   .0132286    .026379     0.50   0.616    -.0385407    .0649979
  econ_change_gdp_mean |  -.0008592   .0039083    -0.22   0.826    -.0085292    .0068108
          econ_fin_max |    .016702    .014169     1.18   0.239    -.0111049    .0445089
   econ_asset_gdp_mean |   1.66e-07   .0003562     0.00   1.000    -.0006988    .0006991
                 _cons |   .0244724   .0339105     0.72   0.471    -.0420776    .0910224
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_2

.         
.   * Model (3)           
.         reghdfe lerner_diff678 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   7,    931) =       3.81
                                                  Prob > F        =     0.0004
                                                  R-squared       =     0.3690
                                                  Adj R-squared   =     0.2809
                                                  Within R-sq.    =     0.0174
                                                  Root MSE        =     0.1208

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0208655   .0080954     2.58   0.010     .0049782    .0367528
   sanc_fpostdur_dummy_sum |   .0086659   .0101263     0.86   0.392    -.0112071     .028539
    sanc_nonfdur_dummy_sum |   .0147757   .0077738     1.90   0.058    -.0004805    .0300318
sanc_nonfpostdur_dummy_sum |    .005061   .0049505     1.02   0.307    -.0046544    .0147764
             sanc_type_max |  -.0199022   .0104228    -1.91   0.057    -.0403571    .0005527
            sanc_state_max |   .0072463   .0030965     2.34   0.019     .0011695    .0133231
              sanc_org_max |  -.0006035   .0284329    -0.02   0.983    -.0564035    .0551966
                     _cons |   .0269523   .0072545     3.72   0.000     .0127152    .0411893
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_3

. 
.   * Model (4)           
.         reghdfe lerner_diff678 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(dropped 1 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(  10,    928) =       3.15
                                                  Prob > F        =     0.0006
                                                  R-squared       =     0.3710
                                                  Adj R-squared   =     0.2809
                                                  Within R-sq.    =     0.0205
                                                  Root MSE        =     0.1208

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0231741   .0085522     2.71   0.007     .0063903    .0399579
   sanc_fpostdur_dummy_sum |   .0094758   .0101099     0.94   0.349    -.0103652    .0293168
    sanc_nonfdur_dummy_sum |    .015132   .0078148     1.94   0.053    -.0002046    .0304687
sanc_nonfpostdur_dummy_sum |   .0054461   .0049521     1.10   0.272    -.0042726    .0151648
             sanc_type_max |  -.0203512   .0104244    -1.95   0.051    -.0408093    .0001068
            sanc_state_max |   .0072127   .0030835     2.34   0.020     .0011612    .0132642
              sanc_org_max |  -.0035331   .0286958    -0.12   0.902    -.0598492     .052783
      econ_change_gdp_mean |  -.0013672   .0038915    -0.35   0.725    -.0090043    .0062699
              econ_fin_max |   .0177363   .0140552     1.26   0.207    -.0098474    .0453199
       econ_asset_gdp_mean |   .0000634   .0003591     0.18   0.860    -.0006413    .0007681
                     _cons |   .0198352   .0331141     0.60   0.549     -.045152    .0848224
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_4

.         
.   * Export regression table     
.     esttab diff678_dur_ols_1 diff678_dur_ols_2 ///
>                diff678_dur_ols_3 diff678_dur_ols_4 ///
>                using "[Appendix 10] diff678_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace 
(file [Appendix 10] diff678_dur_ols.rtf not found)
(output written to [Appendix 10] diff678_dur_ols.rtf)

. 
.         
. ** Appx. 11. Pre-1996: Excluded / Sample: Diff: 7-9 Yr / IV: Duration / OLS ****
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Excluded.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.04
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5411
                                                  Adj R-squared   =     0.5055
                                                  Within R-sq.    =     0.0403
                                                  Root MSE        =     0.1007

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0059036    .002161     2.73   0.006     .0016653    .0101418
   sanc_fpostdur |   -.012776    .002738    -4.67   0.000    -.0181459    -.007406
    sanc_nonfdur |  -.0016411   .0017866    -0.92   0.358    -.0051452    .0018629
sanc_nonfpostdur |  -.0027412   .0017611    -1.56   0.120    -.0061952    .0007128
       sanc_type |  -.0053349   .0047586    -1.12   0.262    -.0146676    .0039979
      sanc_state |  -.0053158   .0016218    -3.28   0.001    -.0084966    -.002135
        sanc_org |  -.0149382   .0142214    -1.05   0.294    -.0428298    .0129534
 econ_change_gdp |   .0033616   .0008998     3.74   0.000     .0015969    .0051262
        econ_fin |  -.0065384   .0070577    -0.93   0.354    -.0203802    .0073035
  econ_asset_gdp |  -.0002611   .0001415    -1.85   0.065    -.0005387    .0000164
           _cons |   .2698271   .0096025    28.10   0.000     .2509943    .2886599
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,003 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 6) by(iso3_num)   

. 
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff789 = (F7.lerner + F8.lerner + F9.lerner)/3 - lerner 
(1,051 missing values generated)

. 
.   * Model (1)   
.         reghdfe lerner_diff789 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   5,    820) =       3.61
                                                  Prob > F        =     0.0031
                                                  R-squared       =     0.4473
                                                  Adj R-squared   =     0.3617
                                                  Within R-sq.    =     0.0165
                                                  Root MSE        =     0.1156

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0145019   .0060858     2.38   0.017     .0025564    .0264474
sanc_postdur_dummy_sum |    .007669   .0060499     1.27   0.205    -.0042061    .0195441
         sanc_type_max |  -.0056966   .0074185    -0.77   0.443    -.0202581    .0088649
        sanc_state_max |   .0042204    .002505     1.68   0.092    -.0006965    .0091373
          sanc_org_max |   .0398842   .0260527     1.53   0.126    -.0112536     .091022
                 _cons |   .0299781   .0078725     3.81   0.000     .0145255    .0454307
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_1

. 
.   * Model (2)   
.         reghdfe lerner_diff789 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   8,    817) =       2.29
                                                  Prob > F        =     0.0199
                                                  R-squared       =     0.4479
                                                  Adj R-squared   =     0.3601
                                                  Within R-sq.    =     0.0177
                                                  Root MSE        =     0.1158

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0148665    .006071     2.45   0.015     .0029499     .026783
sanc_postdur_dummy_sum |   .0075688   .0060865     1.24   0.214    -.0043783    .0195159
         sanc_type_max |  -.0058524    .007619    -0.77   0.443    -.0208074    .0091026
        sanc_state_max |    .004244   .0026128     1.62   0.105    -.0008845    .0093726
          sanc_org_max |   .0403614   .0262461     1.54   0.124    -.0111563    .0918791
  econ_change_gdp_mean |    .002104   .0046757     0.45   0.653    -.0070739    .0112818
          econ_fin_max |   .0121753   .0121297     1.00   0.316    -.0116338    .0359845
   econ_asset_gdp_mean |   -.000082   .0003917    -0.21   0.834    -.0008509    .0006869
                 _cons |   .0191651   .0384677     0.50   0.618    -.0563421    .0946723
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_2

.         
.   * Model (3)   
.         reghdfe lerner_diff789 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   7,    818) =       5.26
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4502
                                                  Adj R-squared   =     0.3635
                                                  Within R-sq.    =     0.0218
                                                  Root MSE        =     0.1155

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0224338   .0080615     2.78   0.006     .0066101    .0382575
   sanc_fpostdur_dummy_sum |   .0103375   .0110345     0.94   0.349    -.0113219    .0319969
    sanc_nonfdur_dummy_sum |   .0119723   .0065273     1.83   0.067    -.0008398    .0247845
sanc_nonfpostdur_dummy_sum |   .0051953    .005577     0.93   0.352    -.0057517    .0161423
             sanc_type_max |  -.0113277    .007373    -1.54   0.125       -.0258    .0031446
            sanc_state_max |   .0045908   .0026195     1.75   0.080    -.0005509    .0097325
              sanc_org_max |   .0260046   .0275866     0.94   0.346    -.0281443    .0801534
                     _cons |   .0273543   .0080149     3.41   0.001     .0116221    .0430864
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_3

.         
.   * Model (4)
.         reghdfe lerner_diff789 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(  10,    815) =       3.76
                                                  Prob > F        =     0.0001
                                                  R-squared       =     0.4509
                                                  Adj R-squared   =     0.3620
                                                  Within R-sq.    =     0.0231
                                                  Root MSE        =     0.1156

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |    .023157   .0086091     2.69   0.007     .0062584    .0400557
   sanc_fpostdur_dummy_sum |   .0101948    .011114     0.92   0.359    -.0116206    .0320102
    sanc_nonfdur_dummy_sum |   .0121047   .0065239     1.86   0.064    -.0007009    .0249102
sanc_nonfpostdur_dummy_sum |   .0054571   .0055672     0.98   0.327    -.0054707    .0163849
             sanc_type_max |  -.0113811   .0074357    -1.53   0.126    -.0259764    .0032142
            sanc_state_max |   .0046064   .0026623     1.73   0.084    -.0006192    .0098321
              sanc_org_max |    .024735   .0282156     0.88   0.381    -.0306488    .0801189
      econ_change_gdp_mean |   .0013637   .0046592     0.29   0.770    -.0077817    .0105091
              econ_fin_max |   .0138264   .0120114     1.15   0.250    -.0097505    .0374032
       econ_asset_gdp_mean |  -.0000128   .0003942    -0.03   0.974    -.0007865    .0007609
                     _cons |   .0147836   .0374369     0.39   0.693    -.0587005    .0882677
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_4

.         
.   * Export regression table     
.     esttab diff789_dur_ols_1 diff789_dur_ols_2 ///
>                diff789_dur_ols_3 diff789_dur_ols_4 ///
>                using "[Appendix 11] diff789_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace         
(file [Appendix 11] diff789_dur_ols.rtf not found)
(output written to [Appendix 11] diff789_dur_ols.rtf)

. 
.         
. ** Appx. 12. Pre-1996: Included / Summary Statistics ***************************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   *     Appx. 12. Summary Statistics
.     asdoc sum lerner ///
>                   sanc_dur sanc_postdur sanc_dur_dummy ///
>                           sanc_fdur sanc_fpostdur sanc_fdur_dummy ///
>                           sanc_nonfdur sanc_nonfpostdur sanc_nonfdur_dummy ///
>                           sanc_type sanc_state sanc_org ///
>                           econ_change_gdp econ_lgdppc econ_lgdp econ_fin econ_asset_gdp ///
>                           pol_polity2dem  ///
>                           econ_fdi_gdp econ_trade_gdp, ///
>                           save([Appendix 12] Summary Statistics.doc) replace

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      lerner |      1,999    .2557181    .1432471   -1.60869    1.07559
    sanc_dur |      1,999    3.006003    7.785665          0         65
sanc_postdur |      1,999     .961981    2.816645          0         17
sanc_dur_d~y |      1,999    .2591296    .4382665          0          1
   sanc_fdur |      1,999     1.01901    3.916501          0         35
-------------+---------------------------------------------------------
sanc_fpost~r |      1,999    .5892946     2.28848          0         17
sanc_fdur_~y |      1,999    .1130565    .3167411          0          1
sanc_nonfdur |      1,999    2.795898    7.690742          0         65
sanc_nonfp~r |      1,999    .8389195    2.632869          0         17
sanc_nonfd~y |      1,999    .2361181    .4248018          0          1
-------------+---------------------------------------------------------
   sanc_type |      1,999    .5702851    1.243577          0          6
  sanc_state |      1,999    .6393197    2.206659          0         16
    sanc_org |      1,999     .126063    .3320034          0          1
econ_chang~p |      1,999    4.015137    4.089033  -17.00469       34.5
 econ_lgdppc |      1,999    8.564123    1.553591   4.704661   11.72544
-------------+---------------------------------------------------------
   econ_lgdp |      1,999    24.88218    1.900457   20.27051   30.49611
    econ_fin |      1,999    .2716358    .4449144          0          1
econ_asset~p |      1,999    63.70258    47.37141   .4382711   305.2436
pol_polity~m |      1,845    .6769648    .4677628          0          1
econ_fdi_gdp |      1,989    .0586874    .2019476  -.5753231   4.490828
-------------+---------------------------------------------------------
econ_trade~p |      1,869    .8477326    .5590917   .0303081   4.426249
Click to Open File:  [Appendix 12] Summary Statistics.doc

.                           
. 
. ** Appx. 13. Pre-1996: Included / Sample: Full / IV: Duration / OLS ************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Model (1)
.         reghdfe lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   5,   1859) =       8.09
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5267
                                                  Adj R-squared   =     0.4914
                                                  Within R-sq.    =     0.0103
                                                  Root MSE        =     0.1022

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    sanc_dur |   -.000904   .0007674    -1.18   0.239     -.002409    .0006011
sanc_postdur |   .0014499   .0014335     1.01   0.312    -.0013616    .0042613
   sanc_type |   .0057159   .0042287     1.35   0.177    -.0025776    .0140093
  sanc_state |  -.0078912   .0016008    -4.93   0.000    -.0110308   -.0047517
    sanc_org |   .0013446   .0153462     0.09   0.930    -.0287531    .0314422
       _cons |   .2586566   .0037945    68.17   0.000     .2512146    .2660985
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_1

.         
.   * Model (2)
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       6.93
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5350
                                                  Adj R-squared   =     0.4994
                                                  Within R-sq.    =     0.0275
                                                  Root MSE        =     0.1014

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
       sanc_dur |  -.0010962   .0007709    -1.42   0.155     -.002608    .0004157
   sanc_postdur |   .0011467   .0014169     0.81   0.418    -.0016322    .0039256
      sanc_type |   .0070768   .0043233     1.64   0.102    -.0014023    .0155559
     sanc_state |  -.0083023   .0016327    -5.08   0.000    -.0115044   -.0051001
       sanc_org |   .0011954   .0153882     0.08   0.938    -.0289845    .0313753
econ_change_gdp |   .0032951   .0009105     3.62   0.000     .0015093    .0050809
       econ_fin |  -.0045416   .0070286    -0.65   0.518    -.0183264    .0092433
 econ_asset_gdp |  -.0003077   .0001439    -2.14   0.033      -.00059   -.0000254
          _cons |   .2666363   .0103106    25.86   0.000     .2464148    .2868578
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_2

. 
.   * Model (3)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   7,   1857) =      11.83
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5345
                                                  Adj R-squared   =     0.4992
                                                  Within R-sq.    =     0.0265
                                                  Root MSE        =     0.1014

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0040762   .0010198     4.00   0.000     .0020762    .0060762
   sanc_fpostdur |    -.00568   .0018854    -3.01   0.003    -.0093776   -.0019824
    sanc_nonfdur |  -.0025937    .000864    -3.00   0.003    -.0042883   -.0008991
sanc_nonfpostdur |   .0004025   .0014709     0.27   0.784    -.0024822    .0032873
       sanc_type |  -.0018749   .0040297    -0.47   0.642    -.0097781    .0060283
      sanc_state |  -.0066001   .0015579    -4.24   0.000    -.0096555   -.0035448
        sanc_org |  -.0047419   .0151956    -0.31   0.755    -.0345441    .0250603
           _cons |   .2677122   .0039178    68.33   0.000     .2600285    .2753959
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_dur_ols_3

. 
.   * Model (4)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_4

.         
.   * Export regression table
.     esttab full_dur_ols_1  full_dur_ols_2  ///
>                full_dur_ols_3  full_dur_ols_4  ///
>                using "[Appendix 13] full_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace       
(file [Appendix 13] full_dur_ols.rtf not found)
(output written to [Appendix 13] full_dur_ols.rtf)

.         
.         
. ** Appx. 14. Pre-1996: Included / Sample: Full / IV: None / OLS ****************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

.         
.   * Model (1)
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   3,   1861) =      12.81
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5260
                                                  Adj R-squared   =     0.4911
                                                  Within R-sq.    =     0.0087
                                                  Root MSE        =     0.1022

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   sanc_type |   .0032472   .0039973     0.81   0.417    -.0045926    .0110869
  sanc_state |  -.0078105   .0016007    -4.88   0.000    -.0109498   -.0046712
    sanc_org |   .0018364   .0152911     0.12   0.904    -.0281531     .031826
       _cons |   .2586282   .0027745    93.22   0.000     .2531867    .2640698
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_1

. 
.   * Model (2)   
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   6,   1858) =       8.79
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5342
                                                  Adj R-squared   =     0.4991
                                                  Within R-sq.    =     0.0258
                                                  Root MSE        =     0.1014

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
      sanc_type |   .0044555   .0041061     1.09   0.278    -.0035976    .0125086
     sanc_state |  -.0082033   .0016327    -5.02   0.000    -.0114055   -.0050012
       sanc_org |   .0018695   .0153088     0.12   0.903    -.0281548    .0318938
econ_change_gdp |   .0033311   .0009103     3.66   0.000     .0015458    .0051164
       econ_fin |  -.0051747   .0070014    -0.74   0.460    -.0189061    .0085567
 econ_asset_gdp |  -.0002858   .0001416    -2.02   0.044    -.0005636   -8.02e-06
          _cons |   .2644226   .0096097    27.52   0.000     .2455756    .2832696
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_2

.         
.   * Model (3)           
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   3,   1861) =      12.81
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5260
                                                  Adj R-squared   =     0.4911
                                                  Within R-sq.    =     0.0087
                                                  Root MSE        =     0.1022

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   sanc_type |   .0032472   .0039973     0.81   0.417    -.0045926    .0110869
  sanc_state |  -.0078105   .0016007    -4.88   0.000    -.0109498   -.0046712
    sanc_org |   .0018364   .0152911     0.12   0.904    -.0281531     .031826
       _cons |   .2586282   .0027745    93.22   0.000     .2531867    .2640698
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_no_ols_3

.         
.   * Model (4)
.         reghdfe lerner ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   6,   1858) =       8.79
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5342
                                                  Adj R-squared   =     0.4991
                                                  Within R-sq.    =     0.0258
                                                  Root MSE        =     0.1014

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
      sanc_type |   .0044555   .0041061     1.09   0.278    -.0035976    .0125086
     sanc_state |  -.0082033   .0016327    -5.02   0.000    -.0114055   -.0050012
       sanc_org |   .0018695   .0153088     0.12   0.903    -.0281548    .0318938
econ_change_gdp |   .0033311   .0009103     3.66   0.000     .0015458    .0051164
       econ_fin |  -.0051747   .0070014    -0.74   0.460    -.0189061    .0085567
 econ_asset_gdp |  -.0002858   .0001416    -2.02   0.044    -.0005636   -8.02e-06
          _cons |   .2644226   .0096097    27.52   0.000     .2455756    .2832696
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_no_ols_4 

.         
.   * Export regression table
.     esttab full_no_ols_1  full_no_ols_2  ///
>                full_no_ols_3  full_no_ols_4  ///
>                using "[Appendix 14] full_no_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace       
(file [Appendix 14] full_no_ols.rtf not found)
(output written to [Appendix 14] full_no_ols.rtf)

.         
. 
. ** Appx. 15. Pre-1996: Included / Sample: Full / IV: Duration / OLS ************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Model (1)
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdppc econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       6.13
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5300
                                                  Adj R-squared   =     0.4941
                                                  Within R-sq.    =     0.0171
                                                  Root MSE        =     0.1019

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      sanc_dur |  -.0010495   .0007653    -1.37   0.170    -.0025505    .0004514
  sanc_postdur |   .0013731   .0014401     0.95   0.340    -.0014513    .0041975
     sanc_type |   .0058918   .0042516     1.39   0.166    -.0024467    .0142303
    sanc_state |  -.0077801   .0016167    -4.81   0.000    -.0109509   -.0046093
      sanc_org |   .0006568   .0154469     0.04   0.966    -.0296383    .0309519
   econ_lgdppc |  -.0116828   .0123781    -0.94   0.345    -.0359593    .0125937
      econ_fin |  -.0110064   .0071185    -1.55   0.122    -.0249676    .0029548
econ_asset_gdp |  -.0003832    .000162    -2.37   0.018     -.000701   -.0000655
         _cons |   .3865389   .1032261     3.74   0.000     .1840875    .5889903
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_gdppc_1

.         
.   * Model (2)
.         reghdfe lerner sanc_dur sanc_postdur  ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       5.96
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5299
                                                  Adj R-squared   =     0.4939
                                                  Within R-sq.    =     0.0169
                                                  Root MSE        =     0.1019

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      sanc_dur |  -.0010881    .000762    -1.43   0.153    -.0025825    .0004064
  sanc_postdur |   .0012948   .0014301     0.91   0.365    -.0015098    .0040995
     sanc_type |     .00598   .0042245     1.42   0.157    -.0023053    .0142653
    sanc_state |  -.0078135   .0016133    -4.84   0.000    -.0109776   -.0046494
      sanc_org |   .0005801   .0154151     0.04   0.970    -.0296526    .0308129
     econ_lgdp |  -.0086902   .0118056    -0.74   0.462    -.0318439    .0144635
      econ_fin |  -.0107318   .0072749    -1.48   0.140    -.0249997    .0035361
econ_asset_gdp |  -.0003919   .0001595    -2.46   0.014    -.0007047   -.0000791
         _cons |   .5033667   .2919391     1.72   0.085    -.0691968     1.07593
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_lgdp_1   

.         
.   * Model (3)   
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdppc econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.33
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5377
                                                  Adj R-squared   =     0.5018
                                                  Within R-sq.    =     0.0331
                                                  Root MSE        =     0.1011

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0038653   .0010221     3.78   0.000     .0018607    .0058699
   sanc_fpostdur |  -.0061177   .0019749    -3.10   0.002     -.009991   -.0022444
    sanc_nonfdur |  -.0027131     .00087    -3.12   0.002    -.0044194   -.0010069
sanc_nonfpostdur |   .0002246   .0014732     0.15   0.879    -.0026647    .0031139
       sanc_type |  -.0014942   .0040433    -0.37   0.712    -.0094242    .0064358
      sanc_state |  -.0064056    .001573    -4.07   0.000    -.0094906   -.0033205
        sanc_org |  -.0059991   .0152904    -0.39   0.695    -.0359874    .0239892
     econ_lgdppc |   .0004218   .0125787     0.03   0.973     -.024248    .0250917
        econ_fin |  -.0136942   .0071821    -1.91   0.057    -.0277801    .0003917
  econ_asset_gdp |   -.000359   .0001588    -2.26   0.024    -.0006704   -.0000477
           _cons |   .2914635   .1043072     2.79   0.005     .0868916    .4960355
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_gdppc_2

.         
.   * Model (4)
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_lgdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =       9.01
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5377
                                                  Adj R-squared   =     0.5018
                                                  Within R-sq.    =     0.0332
                                                  Root MSE        =     0.1011

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |   .0038794   .0010199     3.80   0.000     .0018791    .0058797
   sanc_fpostdur |  -.0062353   .0019784    -3.15   0.002    -.0101154   -.0023551
    sanc_nonfdur |  -.0027547   .0008661    -3.18   0.001    -.0044534    -.001056
sanc_nonfpostdur |   .0002058   .0014615     0.14   0.888    -.0026606    .0030721
       sanc_type |  -.0014253   .0040324    -0.35   0.724    -.0093337    .0064832
      sanc_state |  -.0063971   .0015752    -4.06   0.000    -.0094865   -.0033077
        sanc_org |  -.0061171    .015275    -0.40   0.689     -.036075    .0238409
       econ_lgdp |    .004278    .011931     0.36   0.720    -.0191215    .0276775
        econ_fin |  -.0131151   .0073253    -1.79   0.074    -.0274817    .0012515
  econ_asset_gdp |  -.0003621   .0001562    -2.32   0.021    -.0006685   -.0000557
           _cons |   .1888228   .2943934     0.64   0.521    -.3885546    .7662002
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_dur_ols_lgdp_2

.         
.   * Export regression table
.     esttab full_dur_ols_gdppc_1 full_dur_ols_lgdp_1  ///
>                full_dur_ols_gdppc_2 full_dur_ols_lgdp_2 ///
>                using "[Appendix 15] full_dur_ols_gdppc_lgdp.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_lgdppc econ_lgdp econ_fin econ_asset_gdp) replace         
(file [Appendix 15] full_dur_ols_gdppc_lgdp.rtf not found)
(output written to [Appendix 15] full_dur_ols_gdppc_lgdp.rtf)

.         
.         
. ** Appx. 16. Pre-1996: Included / Sample: Full / IV: Dummy / OLS ***************
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Model (1)                   
.         reghdfe lerner sanc_dur_dummy ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   4,   1860) =      10.32
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5285
                                                  Adj R-squared   =     0.4935
                                                  Within R-sq.    =     0.0139
                                                  Root MSE        =     0.1019

--------------------------------------------------------------------------------
               |               Robust
        lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
sanc_dur_dummy |  -.0358824   .0130249    -2.75   0.006    -.0614273   -.0103374
     sanc_type |   .0111301   .0045345     2.45   0.014     .0022368    .0200234
    sanc_state |  -.0078875   .0016279    -4.85   0.000    -.0110801   -.0046948
      sanc_org |   .0087479     .01632     0.54   0.592    -.0232595    .0407553
         _cons |   .2626088   .0029454    89.16   0.000     .2568322    .2683854
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_1

.         
.   * Model (2)   
.         reghdfe lerner sanc_dur_dummy ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   7,   1857) =       8.15
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5367
                                                  Adj R-squared   =     0.5015
                                                  Within R-sq.    =     0.0311
                                                  Root MSE        =     0.1011

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
 sanc_dur_dummy |  -.0359597   .0131115    -2.74   0.006    -.0616744   -.0102449
      sanc_type |   .0123424   .0046593     2.65   0.008     .0032044    .0214804
     sanc_state |  -.0082591   .0016616    -4.97   0.000     -.011518   -.0050003
       sanc_org |   .0087437   .0163009     0.54   0.592    -.0232263    .0407137
econ_change_gdp |   .0033053   .0009135     3.62   0.000     .0015136    .0050969
       econ_fin |  -.0060328   .0069829    -0.86   0.388    -.0197279    .0076624
 econ_asset_gdp |  -.0002846   .0001417    -2.01   0.045    -.0005626   -6.68e-06
          _cons |   .2686756   .0098404    27.30   0.000     .2493761    .2879751
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_2

.         
.   * Model (3)           
.         reghdfe lerner sanc_fdur_dummy sanc_nonfdur_dummy ///
>              sanc_type sanc_state sanc_org, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   5,   1859) =      12.05
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5319
                                                  Adj R-squared   =     0.4969
                                                  Within R-sq.    =     0.0211
                                                  Root MSE        =     0.1016

------------------------------------------------------------------------------------
                   |               Robust
            lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
   sanc_fdur_dummy |   .0507237   .0154372     3.29   0.001     .0204476    .0809997
sanc_nonfdur_dummy |  -.0368374   .0130171    -2.83   0.005     -.062367   -.0113078
         sanc_type |    .004308     .00501     0.86   0.390    -.0055177    .0141338
        sanc_state |  -.0074409   .0015997    -4.65   0.000    -.0105783   -.0043034
          sanc_org |  -.0111728   .0172489    -0.65   0.517    -.0450021    .0226565
             _cons |   .2623902   .0029552    88.79   0.000     .2565944     .268186
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.     est store full_imposition_ols_3

.         
.   * Model (4)
.         reghdfe lerner sanc_fdur_dummy sanc_nonfdur_dummy ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(   8,   1856) =       9.95
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5399
                                                  Adj R-squared   =     0.5047
                                                  Within R-sq.    =     0.0379
                                                  Root MSE        =     0.1008

------------------------------------------------------------------------------------
                   |               Robust
            lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
   sanc_fdur_dummy |   .0486375   .0154099     3.16   0.002     .0184149    .0788601
sanc_nonfdur_dummy |  -.0374743   .0130931    -2.86   0.004    -.0631532   -.0117955
         sanc_type |   .0059718   .0051329     1.16   0.245    -.0040952    .0160387
        sanc_state |  -.0078616   .0016187    -4.86   0.000    -.0110362    -.004687
          sanc_org |  -.0105551   .0172215    -0.61   0.540    -.0443306    .0232204
   econ_change_gdp |   .0033666   .0009068     3.71   0.000     .0015882    .0051449
          econ_fin |  -.0053159   .0069774    -0.76   0.446    -.0190003    .0083685
    econ_asset_gdp |  -.0002558   .0001406    -1.82   0.069    -.0005317      .00002
             _cons |   .2662433   .0096941    27.46   0.000     .2472309    .2852558
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.         est store full_imposition_ols_4 

. 
.   * Export regression table     
.     esttab full_imposition_ols_1  full_imposition_ols_2  ///
>            full_imposition_ols_3  full_imposition_ols_4 ///     
>                using "[Appendix 16] full_imposition_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy  ///
>                          sanc_fdur_dummy sanc_nonfdur_dummy ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp) replace               
(file [Appendix 16] full_imposition_ols.rtf not found)
(output written to [Appendix 16] full_imposition_ols.rtf)

.         
.         
. ** Appx. 17. Pre-1996: Included / Sample: Sanc. States / IV: Duration / Heckman 
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Preparatory Steps (7): Restrict sample to the countries that experienced sanctions
.     egen exp_sanc = max(sanc_dur_dummy), by(iso3_num)

.     keep if exp_sanc == 1       
(925 observations deleted)

.         
.   * Model (1)
.     heckman lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                         
>                                  
note: sanc_postdur omitted because of collinearity.

Iteration 0:  Log pseudolikelihood =  270.38604  
Iteration 1:  Log pseudolikelihood =  275.42856  
Iteration 2:  Log pseudolikelihood =  278.62759  
Iteration 3:  Log pseudolikelihood =  278.67081  
Iteration 4:  Log pseudolikelihood =  278.67085  
Iteration 5:  Log pseudolikelihood =  278.67085  

Heckman selection model                         Number of obs     =        916
(regression model with sample selection)              Selected    =        419
                                                      Nonselected =        497

                                                Wald chi2(72)     =          .
Log pseudolikelihood =  278.6709                Prob > chi2       =          .

--------------------------------------------------------------------------------
               |               Robust
               | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
lerner         |
      sanc_dur |  -.0061711   .0023624    -2.61   0.009    -.0108013   -.0015408
  sanc_postdur |          0  (omitted)
     sanc_type |   .0297412    .008531     3.49   0.000     .0130207    .0464616
    sanc_state |  -.0127576   .0039711    -3.21   0.001    -.0205407   -.0049744
      sanc_org |  -.0853827   .0416561    -2.05   0.040    -.1670272   -.0037382
               |
      iso3_num |
          ARG  |  -.5161541   .1603145    -3.22   0.001    -.8303647   -.2019435
          ARM  |  -.0937861   .0438606    -2.14   0.032    -.1797513    -.007821
          AZE  |   .0273427   .0481784     0.57   0.570    -.0670851    .1217706
          BEN  |  -.2639796   .0590411    -4.47   0.000    -.3796981   -.1482611
          BGR  |  -.1925925   .0506248    -3.80   0.000    -.2918153   -.0933698
          BLR  |  -.1721207   .0630649    -2.73   0.006    -.2957256   -.0485158
          BOL  |  -.2766313   .0809601    -3.42   0.001    -.4353101   -.1179525
          BRA  |  -.2100447   .0642769    -3.27   0.001    -.3360251   -.0840642
          CAN  |  -.3717936   .0710621    -5.23   0.000    -.5110727   -.2325145
          CHE  |   -.516753   .0873673    -5.91   0.000    -.6879898   -.3455162
          CHN  |  -.0411758   .0443878    -0.93   0.354    -.1281743    .0458227
          CIV  |  -.1811933   .0721346    -2.51   0.012    -.3225746    -.039812
          CMR  |   .0350944   .0499215     0.70   0.482    -.0627499    .1329387
          COD  |  -.3353393   .0448655    -7.47   0.000    -.4232739   -.2474046
          COL  |  -.1740794    .046664    -3.73   0.000    -.2655391   -.0826197
          CRI  |  -.3548396   .0647364    -5.48   0.000    -.4817206   -.2279586
          CYP  |  -.2124779   .0530181    -4.01   0.000    -.3163914   -.1085645
          DOM  |  -.3655996   .0799942    -4.57   0.000    -.5223854   -.2088137
          ECU  |  -.3687667   .0608019    -6.07   0.000    -.4879362   -.2495971
          EST  |  -.4149489    .219479    -1.89   0.059    -.8451199    .0152221
          FRA  |  -.3114044   .0554904    -5.61   0.000    -.4201636   -.2026451
          GEO  |  -.3177059   .0727236    -4.37   0.000    -.4602416   -.1751702
          HND  |  -.2620198   .0508542    -5.15   0.000    -.3616922   -.1623474
          HRV  |  -.2833313   .0656696    -4.31   0.000    -.4120414   -.1546213
          IDN  |  -.2692322   .0793047    -3.39   0.001    -.4246665   -.1137978
          IND  |  -.1197417   .0600441    -1.99   0.046     -.237426   -.0020575
          IRL  |  -.2635782   .0643851    -4.09   0.000    -.3897706   -.1373857
          IRN  |  -.3924838   .0455661    -8.61   0.000    -.4817917   -.3031759
          ISR  |   .0678228   .1178958     0.58   0.565    -.1632487    .2988943
          ITA  |  -.1680281   .0537773    -3.12   0.002    -.2734297   -.0626265
          KEN  |  -.1812737   .0518335    -3.50   0.000    -.2828654   -.0796819
          LBN  |  -.3322651    .045414    -7.32   0.000    -.4212748   -.2432553
          LTU  |  -.2601179   .0832581    -3.12   0.002    -.4233008   -.0969351
          LVA  |  -.2120483   .0618817    -3.43   0.001    -.3333341   -.0907624
          MDA  |  -.0207827   .0763762    -0.27   0.786    -.1704773    .1289118
          MLI  |  -.3939205   .0566149    -6.96   0.000    -.5048837   -.2829572
          MWI  |  -.1389534   .0533339    -2.61   0.009    -.2434858   -.0344209
          NGA  |  -.2947669   .0654051    -4.51   0.000    -.4229585   -.1665753
          NOR  |  -.1848743   .0787292    -2.35   0.019    -.3391807   -.0305678
          NPL  |  -.2622127   .0687609    -3.81   0.000    -.3969815   -.1274439
          PAK  |  -.2331369   .0490008    -4.76   0.000    -.3291767   -.1370972
          PAN  |  -.2476571   .0806611    -3.07   0.002      -.40575   -.0895642
          PER  |  -.1515964   .0564452    -2.69   0.007    -.2622269   -.0409659
          PHL  |  -.2224754   .0519249    -4.28   0.000    -.3242464   -.1207045
          PRY  |  -.3272456   .0578893    -5.65   0.000    -.4407065   -.2137848
          RUS  |  -.4727509   .0834755    -5.66   0.000    -.6363599    -.309142
          SDN  |  -.0963617   .0524991    -1.84   0.066    -.1992581    .0065346
          SLE  |  -.1010062   .0618937    -1.63   0.103    -.2223156    .0203031
          THA  |  -.1955456   .0675036    -2.90   0.004    -.3278502   -.0632409
          TUN  |  -.0149013    .074018    -0.20   0.840     -.159974    .1301714
          TUR  |  -.1841483   .0494973    -3.72   0.000    -.2811614   -.0871353
          UKR  |  -.2640247   .0708424    -3.73   0.000    -.4028732   -.1251761
          USA  |  -.2821374   .0697127    -4.05   0.000    -.4187718    -.145503
          VEN  |  -.2769765   .0730591    -3.79   0.000    -.4201697   -.1337833
          ZAF  |  -.1031095    .078212    -1.32   0.187    -.2564023    .0501833
               |
          year |
         1998  |  -.0337804   .0340537    -0.99   0.321    -.1005244    .0329637
         1999  |    .013591    .048457     0.28   0.779     -.081383     .108565
         2000  |   .0671685   .0404514     1.66   0.097    -.0121148    .1464518
         2001  |   .0548584   .0310132     1.77   0.077    -.0059264    .1156432
         2002  |   .0959264   .0301216     3.18   0.001     .0368892    .1549637
         2003  |   .1047405   .0357506     2.93   0.003     .0346706    .1748104
         2004  |   .1547002   .0301074     5.14   0.000     .0956908    .2137097
         2005  |   .1521648   .0290546     5.24   0.000     .0952188    .2091109
         2006  |   .1490445   .0307305     4.85   0.000     .0888138    .2092751
         2007  |   .1457024   .0339967     4.29   0.000     .0790701    .2123346
         2008  |   .1397325   .0342189     4.08   0.000     .0726646    .2068003
         2009  |   .1441686   .0355125     4.06   0.000     .0745653     .213772
         2010  |   .1565399   .0378352     4.14   0.000     .0823841    .2306956
         2011  |    .168597   .0380632     4.43   0.000     .0939945    .2431995
         2012  |   .1849667    .038788     4.77   0.000     .1089436    .2609897
         2013  |   .1974382   .0396989     4.97   0.000     .1196298    .2752467
         2014  |   .2125642   .0445421     4.77   0.000     .1252632    .2998651
               |
         _cons |   .4135305   .0635984     6.50   0.000     .2888798    .5381811
---------------+----------------------------------------------------------------
sanc_dur_dummy |
sanc_dur_dummy |
           L1. |   2.427783   .1702807    14.26   0.000     2.094039    2.761527
               |
pol_polity2dem |  -1.023374   .2638762    -3.88   0.000    -1.540562   -.5061863
   econ_lgdppc |  -.0092828   .3073957    -0.03   0.976    -.6117673    .5932017
  econ_fdi_gdp |   .9838195   2.535535     0.39   0.698    -3.985737    5.953376
econ_trade_gdp |  -.3448907   .7074989    -0.49   0.626    -1.731563    1.041782
               |
      iso3_num |
          ARG  |   .6961341   .9450588     0.74   0.461    -1.156147    2.548415
          ARM  |    7.40862    .589919    12.56   0.000       6.2524     8.56484
          AZE  |   6.707725   .5788606    11.59   0.000     5.573179     7.84227
          BEN  |   1.358026   .9258363     1.47   0.142    -.4565799    3.172632
          BGR  |   .7632126   .7737211     0.99   0.324    -.7532529    2.279678
          BLR  |   6.716282   .5247677    12.80   0.000     5.687756    7.744808
          BOL  |   .9937339   .6918853     1.44   0.151    -.3623364    2.349804
          BRA  |   .4024591   1.109182     0.36   0.717    -1.771498    2.576416
          CAN  |   1.742871   1.159464     1.50   0.133    -.5296365    4.015378
          CHE  |    .724838   1.332582     0.54   0.586    -1.886974     3.33665
          CHN  |   7.331504   .8182315     8.96   0.000     5.727799    8.935208
          CIV  |   6.593169   .6023675    10.95   0.000      5.41255    7.773787
          CMR  |  -1.023903   .7572606    -1.35   0.176    -2.508107    .4603001
          COD  |   6.664035   .8560927     7.78   0.000     4.986124    8.341946
          COL  |   1.798786   .9523399     1.89   0.059    -.0677655    3.665338
          CRI  |   2.190191   .7641835     2.87   0.004     .6924192    3.687963
          CYP  |   8.699549   1.117014     7.79   0.000     6.510241    10.88886
          DOM  |   .9592705   .7691196     1.25   0.212    -.5481763    2.466717
          DZA  |  -5.599812   .5420077   -10.33   0.000    -6.662127   -4.537496
          ECU  |    .294672   .6773579     0.44   0.664    -1.032925    1.622269
          EST  |   .7315846   .7996925     0.91   0.360    -.8357839    2.298953
          FRA  |   7.706494   1.157013     6.66   0.000     5.438791    9.974197
          GEO  |   .8951291   .6233373     1.44   0.151    -.3265895    2.116848
          HND  |   .7535423    .724962     1.04   0.299    -.6673571    2.174442
          HRV  |   .8774074   .7839665     1.12   0.263    -.6591387    2.413954
          IDN  |   1.689198    1.04149     1.62   0.105    -.3520842    3.730481
          IND  |   1.175734   .8287947     1.42   0.156    -.4486743    2.800141
          IRL  |   7.796579   1.288713     6.05   0.000     5.270748    10.32241
          IRN  |   7.781886   .7609224    10.23   0.000     6.290505    9.273266
          ISR  |   8.486611      1.217     6.97   0.000     6.101335    10.87189
          ITA  |   .8134464   1.092346     0.74   0.456    -1.327512    2.954405
          KEN  |   .9547043   .7535479     1.27   0.205    -.5222224    2.431631
          LBN  |   7.950991   .6842187    11.62   0.000     6.609947    9.292035
          LTU  |   1.053242   .7060218     1.49   0.136     -.330535    2.437019
          LVA  |   .4493947   .7302678     0.62   0.538    -.9819039    1.880693
          MDA  |   1.963193   .5441455     3.61   0.000     .8966878    3.029699
          MLI  |   .1371338   .8232932     0.17   0.868    -1.476491    1.750759
          MRT  |  -5.804139   .5367015   -10.81   0.000    -6.856054   -4.752223
          MWI  |   .4909308   1.026621     0.48   0.633     -1.52121    2.503072
          NGA  |   .7671876   .7635034     1.00   0.315    -.7292516    2.263627
          NOR  |   1.200429   1.325651     0.91   0.365      -1.3978    3.798657
          NPL  |  -.1518434   1.019865    -0.15   0.882    -2.150742    1.847055
          PAK  |   .6340642   .8858656     0.72   0.474      -1.1022    2.370329
          PAN  |   1.214453     .68499     1.77   0.076    -.1281028    2.557009
          PER  |  -.3724463   .7895488    -0.47   0.637    -1.919933    1.175041
          PHL  |    2.12697   .7051195     3.02   0.003     .7449607    3.508978
          PRY  |   .5535658   .8503947     0.65   0.515    -1.113177    2.220309
          ROU  |  -6.041368    .655858    -9.21   0.000    -7.326826    -4.75591
          RUS  |   .4144258   .9144241     0.45   0.650    -1.377813    2.206664
          SDN  |   7.287533   .8737923     8.34   0.000     5.574931    9.000134
          SLE  |   1.019575   .9905308     1.03   0.303    -.9218301    2.960979
          THA  |   1.024718   .7191167     1.42   0.154     -.384725    2.434161
          TUN  |   .2253024   .5045729     0.45   0.655    -.7636423    1.214247
          TUR  |   .5141507   .8420056     0.61   0.541     -1.13615    2.164451
          UKR  |    .847879   .6997663     1.21   0.226    -.5236377    2.219396
          USA  |   1.698812   1.314444     1.29   0.196    -.8774512    4.275074
          VEN  |   1.007022   .7995674     1.26   0.208    -.5601016    2.574145
          ZAF  |   .3250306   .8041204     0.40   0.686    -1.251016    1.901078
               |
          year |
         1998  |   .0335336   .3207148     0.10   0.917    -.5950559    .6621232
         1999  |  -.8759654   .3460602    -2.53   0.011    -1.554231   -.1976999
         2000  |  -.9285071   .3684583    -2.52   0.012    -1.650672   -.2063421
         2001  |   .1011673   .3256432     0.31   0.756    -.5370816    .7394162
         2002  |  -.1358612   .2992229    -0.45   0.650    -.7223274     .450605
         2003  |    .614568    .322108     1.91   0.056    -.0167521    1.245888
         2004  |    .002686   .3541022     0.01   0.994    -.6913415    .6967135
         2005  |   .3461235   .3082476     1.12   0.261    -.2580306    .9502777
         2006  |   .3641484   .4211567     0.86   0.387    -.4613035      1.1896
         2007  |  -.0715372   .3605593    -0.20   0.843    -.7782204     .635146
         2008  |    .360542   .4196719     0.86   0.390    -.4619999    1.183084
         2009  |  -.0492048   .5383637    -0.09   0.927    -1.104378    1.005969
         2010  |   .1841459   .4679277     0.39   0.694    -.7329755    1.101267
         2011  |    .751306   .4590462     1.64   0.102     -.148408     1.65102
         2012  |   .3541366    .521634     0.68   0.497    -.6682471     1.37652
         2013  |   .7680093   .4364473     1.76   0.078    -.0874116     1.62343
         2014  |   .4815449   .4702128     1.02   0.306    -.4400552    1.403145
               |
         _cons |  -1.301153   2.432604    -0.53   0.593    -6.068969    3.466663
---------------+----------------------------------------------------------------
       /athrho |  -.2392492   .0818551    -2.92   0.003    -.3996823   -.0788161
      /lnsigma |  -2.517459   .0990251   -25.42   0.000    -2.711545   -2.323373
---------------+----------------------------------------------------------------
           rho |  -.2347865   .0773429                     -.3796771   -.0786533
         sigma |   .0806643   .0079878                      .0664341    .0979426
        lambda |  -.0189389   .0072382                     -.0331254   -.0047523
--------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 8.54       Prob > chi2 = 0.0035

.         est store sanc_dur_heckman_1 

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.2347865   .0773429    -3.04   0.002    -.3863757   -.0831972
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_1
(results sanc_dur_heckman_1 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.23478646

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .07734288

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .00240012

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  8.5429793

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .00346857

.         
.   * Model (2)   
.     heckman lerner sanc_dur sanc_postdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust) 
note: sanc_postdur omitted because of collinearity.

Iteration 0:  Log pseudolikelihood =  277.96233  
Iteration 1:  Log pseudolikelihood =  283.19606  
Iteration 2:  Log pseudolikelihood =  285.53716  
Iteration 3:  Log pseudolikelihood =  285.55861  
Iteration 4:  Log pseudolikelihood =  285.55863  

Heckman selection model                         Number of obs     =        916
(regression model with sample selection)              Selected    =        419
                                                      Nonselected =        497

                                                Wald chi2(75)     =          .
Log pseudolikelihood =  285.5586                Prob > chi2       =          .

---------------------------------------------------------------------------------
                |               Robust
                | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
lerner          |
       sanc_dur |  -.0053136   .0023909    -2.22   0.026    -.0099996   -.0006275
   sanc_postdur |          0  (omitted)
      sanc_type |   .0291003   .0084775     3.43   0.001     .0124846    .0457159
     sanc_state |   -.012804   .0039786    -3.22   0.001    -.0206019   -.0050061
       sanc_org |  -.0818829   .0397619    -2.06   0.039    -.1598148    -.003951
econ_change_gdp |   .0031469   .0011431     2.75   0.006     .0009065    .0053873
       econ_fin |  -.0172179   .0156209    -1.10   0.270    -.0478342    .0133985
 econ_asset_gdp |   -.000576   .0001831    -3.15   0.002     -.000935    -.000217
                |
       iso3_num |
           ARG  |  -.4940178   .1587479    -3.11   0.002     -.805158   -.1828776
           ARM  |  -.1091893   .0467941    -2.33   0.020     -.200904   -.0174746
           AZE  |  -.0016215   .0512522    -0.03   0.975     -.102074     .098831
           BEN  |   -.253769   .0599766    -4.23   0.000    -.3713209   -.1362171
           BGR  |  -.1551194   .0525518    -2.95   0.003     -.258119   -.0521198
           BLR  |  -.1467966   .0660523    -2.22   0.026    -.2762567   -.0173366
           BOL  |  -.2593833   .0813486    -3.19   0.001    -.4188237    -.099943
           BRA  |  -.1773885   .0684462    -2.59   0.010    -.3115405   -.0432364
           CAN  |  -.2848689   .0744744    -3.83   0.000     -.430836   -.1389018
           CHE  |   -.417082   .0913859    -4.56   0.000    -.5961951    -.237969
           CHN  |  -.0027561   .0527457    -0.05   0.958    -.1061358    .1006236
           CIV  |  -.1606507   .0717437    -2.24   0.025    -.3012658   -.0200356
           CMR  |   .0340456    .050676     0.67   0.502    -.0652775    .1333688
           COD  |  -.3426838   .0452858    -7.57   0.000    -.4314423   -.2539253
           COL  |   -.157965   .0485201    -3.26   0.001    -.2530627   -.0628674
           CRI  |  -.3328656   .0671794    -4.95   0.000    -.4645348   -.2011964
           CYP  |  -.1628232   .0587743    -2.77   0.006    -.2780187   -.0476276
           DOM  |  -.3525891   .0806603    -4.37   0.000    -.5106805   -.1944977
           ECU  |  -.3578389   .0623644    -5.74   0.000    -.4800708   -.2356069
           EST  |  -.4285432   .2141142    -2.00   0.045    -.8481995    -.008887
           FRA  |  -.2544257   .0622789    -4.09   0.000    -.3764901   -.1323613
           GEO  |  -.3033115   .0734781    -4.13   0.000    -.4473259   -.1592972
           HND  |  -.2340314   .0519051    -4.51   0.000    -.3357635   -.1322993
           HRV  |  -.2429599   .0677078    -3.59   0.000    -.3756648    -.110255
           IDN  |   -.252349   .0799118    -3.16   0.002    -.4089731   -.0957248
           IND  |  -.1214371   .0710882    -1.71   0.088    -.2607675    .0178932
           IRL  |   -.162835   .0701386    -2.32   0.020    -.3003041   -.0253659
           IRN  |  -.4027597   .0524109    -7.68   0.000    -.5054831   -.3000363
           ISR  |   .0668851   .1232073     0.54   0.587    -.1745968    .3083669
           ITA  |  -.1373287   .0580639    -2.37   0.018    -.2511319   -.0235255
           KEN  |  -.1596571   .0524997    -3.04   0.002    -.2625547   -.0567596
           LBN  |  -.2494149   .0519062    -4.81   0.000    -.3511492   -.1476805
           LTU  |  -.2305555   .0831913    -2.77   0.006    -.3936076   -.0675035
           LVA  |  -.2324358   .0663939    -3.50   0.000    -.3625654   -.1023062
           MDA  |   .0084755   .0760186     0.11   0.911    -.1405182    .1574693
           MLI  |   -.371304   .0568043    -6.54   0.000    -.4826383   -.2599696
           MWI  |  -.1158499   .0557717    -2.08   0.038    -.2251603   -.0065395
           NGA  |  -.2845249   .0652719    -4.36   0.000    -.4124554   -.1565944
           NOR  |  -.1061625    .080371    -1.32   0.187    -.2636868    .0513619
           NPL  |   -.237473   .0698294    -3.40   0.001    -.3743361   -.1006099
           PAK  |  -.2194713   .0516417    -4.25   0.000     -.320687   -.1182555
           PAN  |  -.2184769    .080989    -2.70   0.007    -.3772124   -.0597415
           PER  |  -.1295732   .0584803    -2.22   0.027    -.2441925   -.0149539
           PHL  |  -.2076867   .0539237    -3.85   0.000    -.3133752   -.1019983
           PRY  |  -.3089491   .0554866    -5.57   0.000    -.4177009   -.2001973
           RUS  |  -.4307838   .0834508    -5.16   0.000    -.5943443   -.2672233
           SDN  |  -.0929388   .0541011    -1.72   0.086    -.1989749    .0130973
           SLE  |  -.0924199   .0623929    -1.48   0.139    -.2147078    .0298679
           THA  |  -.1315978   .0699348    -1.88   0.060    -.2686676     .005472
           TUN  |   .0347308   .0768117     0.45   0.651    -.1158174     .185279
           TUR  |  -.1717059    .052492    -3.27   0.001    -.2745883   -.0688234
           UKR  |   -.187259   .0768351    -2.44   0.015    -.3378531   -.0366649
           USA  |  -.2351962   .0695491    -3.38   0.001    -.3715099   -.0988825
           VEN  |  -.2532957   .0737405    -3.43   0.001    -.3978244   -.1087669
           ZAF  |  -.1014191   .0876024    -1.16   0.247    -.2731165    .0702784
                |
           year |
          1998  |  -.0266451   .0331821    -0.80   0.422    -.0916808    .0383906
          1999  |   .0223714   .0473868     0.47   0.637    -.0705051    .1152478
          2000  |   .0669228   .0396759     1.69   0.092    -.0108404     .144686
          2001  |    .055496   .0316366     1.75   0.079    -.0065106    .1175026
          2002  |   .0864283     .02938     2.94   0.003     .0288446     .144012
          2003  |   .0936476   .0356155     2.63   0.009     .0238424    .1634528
          2004  |    .140328   .0300857     4.66   0.000     .0813612    .1992948
          2005  |   .1386102   .0293563     4.72   0.000     .0810728    .1961475
          2006  |   .1315422    .031459     4.18   0.000     .0698836    .1932007
          2007  |   .1296415   .0349478     3.71   0.000     .0611451     .198138
          2008  |   .1302107   .0340491     3.82   0.000     .0634756    .1969457
          2009  |    .151003   .0354895     4.25   0.000     .0814448    .2205613
          2010  |   .1439892   .0376022     3.83   0.000     .0702903    .2176881
          2011  |   .1619913   .0375357     4.32   0.000     .0884227    .2355599
          2012  |   .1746557     .03851     4.54   0.000     .0991776    .2501338
          2013  |   .1856043   .0398704     4.66   0.000     .1074597    .2637488
          2014  |   .2045971   .0442255     4.63   0.000     .1179167    .2912774
                |
          _cons |   .4090048    .066844     6.12   0.000     .2779929    .5400167
----------------+----------------------------------------------------------------
sanc_dur_dummy  |
 sanc_dur_dummy |
            L1. |   2.423931   .1704098    14.22   0.000     2.089933    2.757928
                |
 pol_polity2dem |   -1.02288    .265307    -3.86   0.000    -1.542873   -.5028883
    econ_lgdppc |  -.0201006   .3075565    -0.07   0.948    -.6229004    .5826991
   econ_fdi_gdp |   .9537878   2.536445     0.38   0.707    -4.017554     5.92513
 econ_trade_gdp |  -.3426364   .7060332    -0.49   0.627    -1.726436    1.041163
                |
       iso3_num |
           ARG  |   .7108532   .9434696     0.75   0.451    -1.138313     2.56002
           ARM  |   6.940313   .5852614    11.86   0.000     5.793222    8.087404
           AZE  |   6.100423   .5748656    10.61   0.000     4.973707    7.227139
           BEN  |    1.34889   .9242652     1.46   0.144    -.4626367    3.160417
           BGR  |   .7720864    .773812     1.00   0.318    -.7445573     2.28873
           BLR  |   6.201319   .5163645    12.01   0.000     5.189263    7.213375
           BOL  |   .9891419   .6918617     1.43   0.153    -.3668821    2.345166
           BRA  |   .4147381   1.108053     0.37   0.708    -1.757006    2.586482
           CAN  |    1.77286   1.158508     1.53   0.126    -.4977741    4.043493
           CHE  |   .7608481   1.332739     0.57   0.568    -1.851273    3.372969
           CHN  |   6.769312     .81082     8.35   0.000     5.180134     8.35849
           CIV  |    5.95413   .5965912     9.98   0.000     4.784833    7.123427
           CMR  |  -1.028881   .7574252    -1.36   0.174    -2.513407    .4556454
           COD  |   6.038687   .8461606     7.14   0.000     4.380242    7.697131
           COL  |   1.809202   .9517623     1.90   0.057    -.0562181    3.674621
           CRI  |   2.195077   .7618727     2.88   0.004     .7018345     3.68832
           CYP  |   7.914639   1.101297     7.19   0.000     5.756137    10.07314
           DOM  |   .9677089   .7682208     1.26   0.208    -.5379763    2.473394
           DZA  |  -5.126002   .5408873    -9.48   0.000    -6.186122   -4.065882
           ECU  |   .3010581   .6764034     0.45   0.656    -1.024668    1.626784
           EST  |   .7380114   .7990178     0.92   0.356    -.8280346    2.304057
           FRA  |   7.153744   1.145002     6.25   0.000     4.909582    9.397905
           GEO  |   .8983432   .6226637     1.44   0.149    -.3220553    2.118742
           HND  |   .7503884   .7251648     1.03   0.301    -.6709085    2.171685
           HRV  |   .8938603   .7829617     1.14   0.254    -.6407165    2.428437
           IDN  |   1.701262    1.05261     1.62   0.106    -.3618155    3.764339
           IND  |   1.163935   .8286033     1.40   0.160    -.4600974    2.787968
           IRL  |   7.210141   1.282796     5.62   0.000     4.695906    9.724375
           IRN  |   7.247867   .7501922     9.66   0.000     5.777517    8.718216
           ISR  |   7.983097    1.21295     6.58   0.000      5.60576    10.36043
           ITA  |   .8416764   1.090414     0.77   0.440    -1.295496    2.978849
           KEN  |   .9448262   .7545981     1.25   0.211    -.5341588    2.423811
           LBN  |   7.449572   .6466804    11.52   0.000     6.182101    8.717042
           LTU  |   1.068015   .7067306     1.51   0.131    -.3171519    2.453181
           LVA  |     .46265    .730542     0.63   0.527     -.969186    1.894486
           MDA  |   1.953439   .5416155     3.61   0.000     .8918921    3.014986
           MLI  |   .1243052   .8239289     0.15   0.880    -1.490566    1.739176
           MRT  |  -5.339901   .5386412    -9.91   0.000    -6.395618   -4.284183
           MWI  |   .4830582   1.026832     0.47   0.638    -1.529496    2.495613
           NGA  |   .7650131   .7631666     1.00   0.316    -.7307659    2.260792
           NOR  |   1.240541   1.324914     0.94   0.349    -1.356242    3.837324
           NPL  |  -.1679573   1.019492    -0.16   0.869    -2.166125    1.830211
           PAK  |   .6313366   .8870382     0.71   0.477    -1.107226    2.369899
           PAN  |   1.223309   .6853536     1.78   0.074    -.1199595    2.566577
           PER  |  -.3638012   .7883111    -0.46   0.644    -1.908863     1.18126
           PHL  |   2.125188   .7054959     3.01   0.003     .7424416    3.507935
           PRY  |   .5613202    .850635     0.66   0.509    -1.105894    2.228534
           ROU  |  -5.530516   .6600987    -8.38   0.000    -6.824286   -4.236746
           RUS  |     .43256   .9092851     0.48   0.634    -1.349606    2.214726
           SDN  |   6.741394   .8683792     7.76   0.000     5.039402    8.443386
           SLE  |    1.00904    .992707     1.02   0.309    -.9366296     2.95471
           THA  |    1.02896   .7193888     1.43   0.153    -.3810162    2.438936
           TUN  |   .2322143   .5045577     0.46   0.645    -.7567006    1.221129
           TUR  |   .5291522    .841071     0.63   0.529    -1.119317    2.177621
           UKR  |   .8468522   .6997319     1.21   0.226    -.5245971    2.218302
           USA  |   1.732451   1.311908     1.32   0.187    -.8388418    4.303744
           VEN  |   1.009077   .7975328     1.27   0.206    -.5540591    2.572212
           ZAF  |   .3370188   .8033936     0.42   0.675    -1.237604    1.911641
                |
           year |
          1998  |   .0327005   .3217805     0.10   0.919    -.5979777    .6633787
          1999  |  -.8719636   .3478229    -2.51   0.012    -1.553684   -.1902432
          2000  |  -.9267321   .3687771    -2.51   0.012    -1.649522   -.2039422
          2001  |   .1005846   .3253747     0.31   0.757    -.5371381    .7383073
          2002  |  -.1361174   .3000214    -0.45   0.650    -.7241486    .4519138
          2003  |   .6147444   .3225229     1.91   0.057    -.0173889    1.246878
          2004  |   .0021021   .3543229     0.01   0.995    -.6923581    .6965623
          2005  |   .3492581   .3082688     1.13   0.257    -.2549377    .9534538
          2006  |    .355987   .4199145     0.85   0.397    -.4670303    1.179004
          2007  |  -.0661911   .3600233    -0.18   0.854    -.7718238    .6394416
          2008  |   .3663688   .4190569     0.87   0.382    -.4549675    1.187705
          2009  |  -.0355872   .5383633    -0.07   0.947     -1.09076    1.019586
          2010  |   .1940422   .4682196     0.41   0.679    -.7236514    1.111736
          2011  |   .7569462   .4601288     1.65   0.100    -.1448897    1.658782
          2012  |   .3657243   .5225086     0.70   0.484    -.6583738    1.389822
          2013  |   .7780128   .4371439     1.78   0.075    -.0787735    1.634799
          2014  |     .50352   .4701473     1.07   0.284    -.4179517    1.424992
                |
          _cons |  -1.223457   2.437427    -0.50   0.616    -6.000725    3.553812
----------------+----------------------------------------------------------------
        /athrho |  -.2245492   .0783571    -2.87   0.004    -.3781263   -.0709721
       /lnsigma |  -2.534929   .0999516   -25.36   0.000     -2.73083   -2.339027
----------------+----------------------------------------------------------------
            rho |  -.2208497   .0745353                     -.3610791   -.0708532
          sigma |   .0792674   .0079229                      .0651652    .0964214
         lambda |  -.0175062   .0068561                     -.0309439   -.0040685
---------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 8.21       Prob > chi2 = 0.0042

.         est store sanc_dur_heckman_2

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.2208497   .0745353    -2.96   0.003    -.3669361   -.0747632
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_2
(results sanc_dur_heckman_2 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.22084967

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .07453526

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .00304634

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  8.2123277

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .00416067

.         
.   * Model (3)   
.     heckman lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                  

Iteration 0:  Log pseudolikelihood =  282.95704  
Iteration 1:  Log pseudolikelihood =   288.3072  
Iteration 2:  Log pseudolikelihood =  289.49604  
Iteration 3:  Log pseudolikelihood =  289.50176  
Iteration 4:  Log pseudolikelihood =  289.50176  

Heckman selection model                         Number of obs     =        916
(regression model with sample selection)              Selected    =        419
                                                      Nonselected =        497

                                                Wald chi2(75)     =          .
Log pseudolikelihood =  289.5018                Prob > chi2       =          .

----------------------------------------------------------------------------------
                 |               Robust
                 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
lerner           |
       sanc_fdur |   .0051847   .0020579     2.52   0.012     .0011513    .0092181
   sanc_fpostdur |  -.0076527   .0022568    -3.39   0.001    -.0120759   -.0032295
    sanc_nonfdur |  -.0054218   .0032638    -1.66   0.097    -.0118186    .0009751
sanc_nonfpostdur |   .0036945   .0036771     1.00   0.315    -.0035125    .0109016
       sanc_type |    .014063    .007595     1.85   0.064    -.0008229    .0289489
      sanc_state |  -.0055622   .0039182    -1.42   0.156    -.0132417    .0021173
        sanc_org |  -.0668421   .0382297    -1.75   0.080    -.1417709    .0080866
                 |
        iso3_num |
            ARG  |  -.5001659   .1607253    -3.11   0.002    -.8151818   -.1851501
            ARM  |  -.0751793    .042601    -1.76   0.078    -.1586758    .0083172
            AZE  |   .0530591   .0413183     1.28   0.199    -.0279232    .1340414
            BEN  |  -.2510222   .0634957    -3.95   0.000    -.3754716   -.1265729
            BGR  |   -.203094   .0618076    -3.29   0.001    -.3242347   -.0819532
            BLR  |  -.2658978   .0654417    -4.06   0.000    -.3941611   -.1376345
            BOL  |  -.2639083   .0852737    -3.09   0.002    -.4310418   -.0967749
            BRA  |  -.2061401   .0609784    -3.38   0.001    -.3256556   -.0866246
            CAN  |  -.3353923   .0706133    -4.75   0.000    -.4737919   -.1969927
            CHE  |  -.4996088   .0930217    -5.37   0.000    -.6819281   -.3172896
            CHN  |   .0378868   .0460845     0.82   0.411    -.0524372    .1282109
            CIV  |  -.2510159   .0693528    -3.62   0.000    -.3869448   -.1150869
            CMR  |   .0141476   .0416717     0.34   0.734    -.0675274    .0958227
            COD  |  -.3188489   .0417263    -7.64   0.000     -.400631   -.2370668
            COL  |  -.2233809   .0505868    -4.42   0.000    -.3225293   -.1242325
            CRI  |  -.3303148   .0631381    -5.23   0.000    -.4540632   -.2065664
            CYP  |  -.1924507   .0527446    -3.65   0.000    -.2958282   -.0890731
            DOM  |  -.3527358   .0843945    -4.18   0.000     -.518146   -.1873256
            ECU  |  -.3707789   .0505953    -7.33   0.000    -.4699439    -.271614
            EST  |  -.4014268   .2162233    -1.86   0.063    -.8252166     .022363
            FRA  |  -.3008253   .0542373    -5.55   0.000    -.4071284   -.1945222
            GEO  |  -.2806587   .0747809    -3.75   0.000    -.4272266   -.1340908
            HND  |  -.2388355   .0595862    -4.01   0.000    -.3556223   -.1220487
            HRV  |  -.2564744    .065917    -3.89   0.000    -.3856693   -.1272795
            IDN  |  -.2010992   .0745093    -2.70   0.007    -.3471347   -.0550637
            IND  |  -.2653706   .0836802    -3.17   0.002    -.4293808   -.1013604
            IRL  |   -.249032   .0616633    -4.04   0.000    -.3698899   -.1281741
            IRN  |  -.4242508   .0452765    -9.37   0.000    -.5129911   -.3355104
            ISR  |   .0403699   .1511142     0.27   0.789    -.2558086    .3365483
            ITA  |  -.1548025   .0490907    -3.15   0.002    -.2510184   -.0585865
            KEN  |  -.2405407   .0612944    -3.92   0.000    -.3606755    -.120406
            LBN  |  -.3205632    .045372    -7.07   0.000    -.4094906   -.2316358
            LTU  |  -.2320071   .0910923    -2.55   0.011    -.4105447   -.0534695
            LVA  |  -.1979943   .0562852    -3.52   0.000    -.3083113   -.0876773
            MDA  |  -.0974766   .0700745    -1.39   0.164      -.23482    .0398668
            MLI  |  -.3472423   .0689196    -5.04   0.000    -.4823222   -.2121624
            MWI  |  -.1566886   .0525258    -2.98   0.003    -.2596372     -.05374
            NGA  |  -.2277387    .061565    -3.70   0.000    -.3484039   -.1070736
            NOR  |  -.1434159   .0839477    -1.71   0.088    -.3079503    .0211185
            NPL  |  -.2350862   .0707914    -3.32   0.001    -.3738348   -.0963375
            PAK  |  -.2903936   .0446192    -6.51   0.000    -.3778457   -.2029415
            PAN  |  -.2348617   .0850678    -2.76   0.006    -.4015914   -.0681319
            PER  |  -.1341578   .0516895    -2.60   0.009    -.2354674   -.0328482
            PHL  |  -.2901429   .0699262    -4.15   0.000    -.4271956   -.1530902
            PRY  |  -.2996078   .0732348    -4.09   0.000    -.4431454   -.1560703
            RUS  |  -.4565529   .0871172    -5.24   0.000    -.6272996   -.2858063
            SDN  |  -.1837306    .050988    -3.60   0.000    -.2836652    -.083796
            SLE  |   -.055843   .0588648    -0.95   0.343    -.1712159    .0595298
            THA  |  -.1754968   .0699927    -2.51   0.012      -.31268   -.0383135
            TUN  |  -.0898016    .081153    -1.11   0.268    -.2488586    .0692553
            TUR  |  -.3074022    .054769    -5.61   0.000    -.4147475   -.2000569
            UKR  |  -.2559593   .0767377    -3.34   0.001    -.4063623   -.1055562
            USA  |  -.2516257   .0695561    -3.62   0.000    -.3879531   -.1152983
            VEN  |  -.2591528    .073388    -3.53   0.000    -.4029907   -.1153149
            ZAF  |  -.1107626   .1019212    -1.09   0.277    -.3105244    .0889992
                 |
            year |
           1998  |  -.0388787   .0325059    -1.20   0.232     -.102589    .0248316
           1999  |   -.006752   .0477109    -0.14   0.887    -.1002635    .0867596
           2000  |   .0561243   .0373834     1.50   0.133    -.0171457    .1293943
           2001  |   .0436574   .0289563     1.51   0.132    -.0130959    .1004107
           2002  |   .0882096   .0319842     2.76   0.006     .0255217    .1508974
           2003  |   .1030353   .0363252     2.84   0.005     .0318392    .1742313
           2004  |   .1534396   .0331581     4.63   0.000      .088451    .2184282
           2005  |   .1519489   .0327683     4.64   0.000     .0877242    .2161736
           2006  |    .148123   .0354747     4.18   0.000     .0785939    .2176522
           2007  |   .1421784   .0397539     3.58   0.000     .0642622    .2200946
           2008  |   .1342426   .0414685     3.24   0.001     .0529658    .2155194
           2009  |   .1406438   .0424073     3.32   0.001     .0575271    .2237606
           2010  |   .1536883   .0447566     3.43   0.001      .065967    .2414097
           2011  |   .1618749   .0462183     3.50   0.000     .0712887    .2524612
           2012  |   .1780885   .0485252     3.67   0.000     .0829808    .2731961
           2013  |   .1828982   .0519304     3.52   0.000     .0811166    .2846798
           2014  |   .1986645   .0553074     3.59   0.000     .0902641     .307065
                 |
           _cons |   .4023541   .0589547     6.82   0.000      .286805    .5179032
-----------------+----------------------------------------------------------------
sanc_dur_dummy   |
  sanc_dur_dummy |
             L1. |   2.418674    .169959    14.23   0.000     2.085561    2.751788
                 |
  pol_polity2dem |  -.9942438   .2639257    -3.77   0.000    -1.511529   -.4769589
     econ_lgdppc |  -.0304373   .3081013    -0.10   0.921    -.6343048    .5734301
    econ_fdi_gdp |    1.02549   2.564369     0.40   0.689    -4.000581    6.051561
  econ_trade_gdp |  -.3431636   .7110298    -0.48   0.629    -1.736756    1.050429
                 |
        iso3_num |
            ARG  |   .6958492   .9543445     0.73   0.466    -1.174632     2.56633
            ARM  |   6.615119   .5687227    11.63   0.000     5.500443    7.729795
            AZE  |   6.001098    .570297    10.52   0.000     4.883336    7.118859
            BEN  |   1.302928   .9254838     1.41   0.159    -.5109869    3.116843
            BGR  |   .7485195   .7753333     0.97   0.334    -.7711058    2.268145
            BLR  |   5.843052   .5056013    11.56   0.000     4.852092    6.834013
            BOL  |   .9515121   .6956062     1.37   0.171     -.411851    2.314875
            BRA  |   .3923609   1.117551     0.35   0.726    -1.797999     2.58272
            CAN  |   1.774477   1.166133     1.52   0.128    -.5111015    4.060055
            CHE  |   .7646664   1.340581     0.57   0.568    -1.862823    3.392156
            CHN  |    6.52753   .8073696     8.08   0.000     4.945115    8.109946
            CIV  |   5.746622   .5903042     9.74   0.000     4.589647    6.903597
            CMR  |  -1.033975   .7607916    -1.36   0.174    -2.525099    .4571491
            COD  |   5.781796   .8522317     6.78   0.000     4.111453     7.45214
            COL  |   1.810747   .9696315     1.87   0.062    -.0896962    3.711189
            CRI  |   2.181533   .7682569     2.84   0.005      .675777    3.687289
            CYP  |   7.781737   1.101997     7.06   0.000     5.621863    9.941611
            DOM  |   .9421307   .7749889     1.22   0.224    -.5768195    2.461081
            DZA  |  -5.127356   .5440934    -9.42   0.000     -6.19376   -4.060953
            ECU  |   .2898622   .6821176     0.42   0.671    -1.047064    1.626788
            EST  |   .6993175   .7982152     0.88   0.381    -.8651555    2.263791
            FRA  |   6.923489   1.152913     6.01   0.000     4.663821    9.183157
            GEO  |   .8683193   .6255118     1.39   0.165    -.3576613      2.0943
            HND  |   .7138861   .7282064     0.98   0.327    -.7133722    2.141144
            HRV  |   .8874616   .7884536     1.13   0.260    -.6578791    2.432802
            IDN  |    1.73139   1.077041     1.61   0.108    -.3795709    3.842352
            IND  |   1.137212   .8349651     1.36   0.173    -.4992894    2.773714
            IRL  |   6.993676    1.27082     5.50   0.000     4.502915    9.484438
            IRN  |   6.927894   .7404687     9.36   0.000     5.476602    8.379186
            ISR  |   7.566795   1.202856     6.29   0.000     5.209241    9.924348
            ITA  |   .8380699   1.097476     0.76   0.445    -1.312944    2.989084
            KEN  |   .9236532    .759404     1.22   0.224    -.5647512    2.412058
            LBN  |   7.107175   .6500618    10.93   0.000     5.833077    8.381273
            LTU  |   1.051173   .7097452     1.48   0.139    -.3399017    2.442248
            LVA  |   .4433083   .7354628     0.60   0.547    -.9981723    1.884789
            MDA  |   1.904342   .5361028     3.55   0.000     .8536002    2.955085
            MLI  |    .102993   .8259806     0.12   0.901    -1.515899    1.721885
            MRT  |  -5.356094   .5419405    -9.88   0.000    -6.418277    -4.29391
            MWI  |   .4408573   1.028806     0.43   0.668    -1.575566     2.45728
            NGA  |   .7379862   .7634027     0.97   0.334    -.7582555    2.234228
            NOR  |   1.237778   1.331047     0.93   0.352    -1.371025    3.846582
            NPL  |  -.1980004   1.022843    -0.19   0.847    -2.202736    1.806735
            PAK  |   .6227185   .8907257     0.70   0.484    -1.123072    2.368509
            PAN  |   1.201021   .6876693     1.75   0.081     -.146786    2.548828
            PER  |   -.366273   .7921771    -0.46   0.644    -1.918912    1.186366
            PHL  |   2.102349   .7040575     2.99   0.003     .7224217    3.482276
            PRY  |   .5311839   .8503166     0.62   0.532    -1.135406    2.197774
            ROU  |  -5.560407   .6666613    -8.34   0.000    -6.867039   -4.253775
            RUS  |   .4172429   .9092915     0.46   0.646    -1.364936    2.199422
            SDN  |   6.369183   .8499103     7.49   0.000      4.70339    8.034977
            SLE  |   .9576427   .9920963     0.97   0.334    -.9868303    2.902116
            THA  |   1.006016   .7132403     1.41   0.158    -.3919089    2.403942
            TUN  |   .2291985   .5077686     0.45   0.652    -.7660096    1.224407
            TUR  |   .5133423   .8484181     0.61   0.545    -1.149527    2.176211
            UKR  |   .8193958   .7014501     1.17   0.243    -.5554212    2.194213
            USA  |   1.735139   1.321591     1.31   0.189    -.8551324     4.32541
            VEN  |   1.015395   .8042052     1.26   0.207    -.5608181    2.591608
            ZAF  |   .3184489   .8108537     0.39   0.695    -1.270795    1.907693
                 |
            year |
           1998  |   .0311323   .3231255     0.10   0.923    -.6021822    .6644467
           1999  |  -.8789182    .348848    -2.52   0.012    -1.562648   -.1951887
           2000  |  -.9247203   .3675144    -2.52   0.012    -1.645035   -.2044054
           2001  |   .0957892   .3261993     0.29   0.769    -.5435497     .735128
           2002  |  -.1305791   .3002497    -0.43   0.664    -.7190577    .4578996
           2003  |   .6141226   .3248058     1.89   0.059    -.0224851     1.25073
           2004  |  -.0014329   .3530108    -0.00   0.997    -.6933213    .6904556
           2005  |    .344138    .308994     1.11   0.265    -.2614791    .9497551
           2006  |   .3589943   .4199775     0.85   0.393    -.4641465    1.182135
           2007  |  -.0689356   .3602627    -0.19   0.848    -.7750374    .6371663
           2008  |   .3643898   .4194328     0.87   0.385    -.4576834    1.186463
           2009  |  -.0339192   .5378929    -0.06   0.950     -1.08817    1.020332
           2010  |   .2024602   .4682457     0.43   0.665    -.7152846    1.120205
           2011  |   .7580368   .4618814     1.64   0.101     -.147234    1.663308
           2012  |   .3808431   .5253034     0.72   0.468    -.6487327    1.410419
           2013  |   .7797618   .4385766     1.78   0.075    -.0798325    1.639356
           2014  |   .5046297   .4726781     1.07   0.286    -.4218023    1.431062
                 |
           _cons |  -1.142954   2.441114    -0.47   0.640    -5.927451    3.641542
-----------------+----------------------------------------------------------------
         /athrho |   -.178845   .0740767    -2.41   0.016    -.3240327   -.0336573
        /lnsigma |  -2.546941   .1031815   -24.68   0.000    -2.749173   -2.344708
-----------------+----------------------------------------------------------------
             rho |  -.1769623    .071757                     -.3131488   -.0336445
           sigma |   .0783209   .0080813                      .0639808    .0958751
          lambda |  -.0138598   .0064668                     -.0265346   -.0011851
----------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 5.83       Prob > chi2 = 0.0158

.         est store sanc_dur_heckman_3

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.1769623    .071757    -2.47   0.014    -.3176034   -.0363212
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_3
(results sanc_dur_heckman_3 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.17696226

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .07175698

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .01365806

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  5.8289435

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .01576459

.         
.   * Model (4)   
.     heckman lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp i.iso3_num i.year, ///
>          select(sanc_dur_dummy = l.sanc_dur_dummy pol_polity2dem /// 
>                         econ_lgdppc econ_fdi_gdp econ_trade_gdp i.iso3_num i.year) vce(robust)                   

Iteration 0:  Log pseudolikelihood =  290.88564  
Iteration 1:  Log pseudolikelihood =  295.82796  
Iteration 2:  Log pseudolikelihood =  296.63706  
Iteration 3:  Log pseudolikelihood =  296.64047  
Iteration 4:  Log pseudolikelihood =  296.64047  

Heckman selection model                         Number of obs     =        916
(regression model with sample selection)              Selected    =        419
                                                      Nonselected =        497

                                                Wald chi2(79)     =          .
Log pseudolikelihood =  296.6405                Prob > chi2       =          .

----------------------------------------------------------------------------------
                 |               Robust
                 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
lerner           |
       sanc_fdur |   .0059366   .0020602     2.88   0.004     .0018987    .0099744
   sanc_fpostdur |  -.0066591   .0021633    -3.08   0.002    -.0108992    -.002419
    sanc_nonfdur |  -.0045025   .0033261    -1.35   0.176    -.0110215    .0020165
sanc_nonfpostdur |   .0039904   .0036431     1.10   0.273      -.00315    .0111308
       sanc_type |   .0129354   .0075169     1.72   0.085    -.0017975    .0276683
      sanc_state |  -.0051879   .0038791    -1.34   0.181    -.0127907     .002415
        sanc_org |  -.0614722   .0365621    -1.68   0.093    -.1331326    .0101881
 econ_change_gdp |   .0029867   .0010717     2.79   0.005     .0008861    .0050872
        econ_fin |   -.021188   .0154714    -1.37   0.171    -.0515114    .0091354
  econ_asset_gdp |  -.0005682    .000187    -3.04   0.002    -.0009348   -.0002017
                 |
        iso3_num |
            ARG  |  -.4686709   .1588046    -2.95   0.003    -.7799222   -.1574196
            ARM  |  -.0873694   .0450927    -1.94   0.053    -.1757494    .0010106
            AZE  |   .0213473   .0444923     0.48   0.631    -.0658559    .1085505
            BEN  |  -.2353233   .0642934    -3.66   0.000    -.3613361   -.1093106
            BGR  |  -.1611193   .0625208    -2.58   0.010    -.2836578   -.0385807
            BLR  |  -.2415106   .0672252    -3.59   0.000    -.3732695   -.1097516
            BOL  |  -.2411011   .0858222    -2.81   0.005    -.4093096   -.0728926
            BRA  |  -.1714882   .0653727    -2.62   0.009    -.2996162   -.0433601
            CAN  |  -.2446872   .0745703    -3.28   0.001    -.3908422   -.0985321
            CHE  |  -.3945963   .0968879    -4.07   0.000    -.5844932   -.2046994
            CHN  |   .0702187    .053353     1.32   0.188    -.0343513    .1747887
            CIV  |    -.23169   .0686651    -3.37   0.001    -.3662711   -.0971089
            CMR  |   .0130988   .0421298     0.31   0.756    -.0694742    .0956717
            COD  |  -.3216757   .0416503    -7.72   0.000    -.4033088   -.2400427
            COL  |  -.2035867   .0513938    -3.96   0.000    -.3043167   -.1028567
            CRI  |  -.3035095    .065507    -4.63   0.000    -.4319008   -.1751182
            CYP  |  -.1390678   .0591832    -2.35   0.019    -.2550647   -.0230709
            DOM  |   -.334262   .0853944    -3.91   0.000    -.5016318   -.1668921
            ECU  |  -.3565586   .0525929    -6.78   0.000    -.4596388   -.2534784
            EST  |   -.413018   .2113579    -1.95   0.051    -.8272719    .0012359
            FRA  |   -.240025   .0612623    -3.92   0.000    -.3600969   -.1199531
            GEO  |  -.2589028    .075611    -3.42   0.001    -.4070977   -.1107079
            HND  |   -.207256   .0602306    -3.44   0.001    -.3253057   -.0892062
            HRV  |  -.2112435   .0678975    -3.11   0.002    -.3443202   -.0781669
            IDN  |  -.1847183   .0747252    -2.47   0.013     -.331177   -.0382596
            IND  |  -.2879029   .0914114    -3.15   0.002    -.4670659   -.1087398
            IRL  |  -.1426591   .0683449    -2.09   0.037    -.2766127   -.0087055
            IRN  |  -.4445848   .0523022    -8.50   0.000    -.5470953   -.3420743
            ISR  |   .0387761   .1578461     0.25   0.806    -.2705965    .3481488
            ITA  |  -.1231669   .0542887    -2.27   0.023    -.2295708    -.016763
            KEN  |   -.213083   .0609219    -3.50   0.000    -.3324878   -.0936783
            LBN  |  -.2376031   .0514564    -4.62   0.000    -.3384558   -.1367504
            LTU  |  -.1947746   .0912119    -2.14   0.033    -.3735467   -.0160025
            LVA  |  -.2165535   .0609458    -3.55   0.000     -.336005   -.0971019
            MDA  |  -.0651968   .0685882    -0.95   0.342    -.1996272    .0692335
            MLI  |  -.3195303    .069507    -4.60   0.000    -.4557615   -.1832991
            MWI  |  -.1284969   .0546434    -2.35   0.019    -.2355959   -.0213979
            NGA  |  -.2161689   .0607752    -3.56   0.000    -.3352861   -.0970517
            NOR  |  -.0582442   .0860323    -0.68   0.498    -.2268643     .110376
            NPL  |  -.2054281   .0719809    -2.85   0.004    -.3465081   -.0643482
            PAK  |  -.2840269   .0466947    -6.08   0.000    -.3755467    -.192507
            PAN  |  -.1999596   .0851834    -2.35   0.019    -.3669159   -.0330032
            PER  |  -.1070497   .0532047    -2.01   0.044    -.2113289   -.0027705
            PHL  |  -.2709057   .0704518    -3.85   0.000    -.4089888   -.1328226
            PRY  |  -.2750375    .070643    -3.89   0.000    -.4134951   -.1365798
            RUS  |  -.4069281    .087661    -4.64   0.000    -.5787405   -.2351158
            SDN  |  -.1875182   .0516474    -3.63   0.000    -.2887451   -.0862912
            SLE  |  -.0432844   .0589165    -0.73   0.463    -.1587585    .0721898
            THA  |  -.1069332   .0722241    -1.48   0.139    -.2484899    .0346234
            TUN  |  -.0391516   .0826582    -0.47   0.636    -.2011588    .1228555
            TUR  |  -.2893758   .0558847    -5.18   0.000    -.3989078   -.1798437
            UKR  |  -.1731344   .0824686    -2.10   0.036    -.3347699    -.011499
            USA  |  -.1978904   .0694667    -2.85   0.004    -.3340427   -.0617381
            VEN  |   -.230475   .0742788    -3.10   0.002    -.3760587   -.0848913
            ZAF  |  -.1103991    .109929    -1.00   0.315     -.325856    .1050579
                 |
            year |
           1998  |  -.0315758   .0314396    -1.00   0.315    -.0931964    .0300447
           1999  |   .0010124   .0458389     0.02   0.982    -.0888301    .0908549
           2000  |   .0551279   .0358832     1.54   0.124    -.0152018    .1254576
           2001  |   .0426369   .0289498     1.47   0.141    -.0141038    .0993775
           2002  |   .0767166   .0307219     2.50   0.013     .0165028    .1369303
           2003  |   .0893322    .035687     2.50   0.012     .0193869    .1592775
           2004  |    .136097    .032768     4.15   0.000      .071873    .2003211
           2005  |   .1352656   .0327919     4.12   0.000     .0709946    .1995365
           2006  |   .1273279    .036025     3.53   0.000     .0567201    .1979356
           2007  |   .1221918   .0404993     3.02   0.003     .0428147    .2015689
           2008  |    .120286   .0409987     2.93   0.003     .0399299     .200642
           2009  |   .1422755   .0419512     3.39   0.001     .0600527    .2244984
           2010  |   .1359672   .0446261     3.05   0.002     .0485016    .2234327
           2011  |   .1503995   .0460422     3.27   0.001     .0601584    .2406406
           2012  |   .1620385   .0484903     3.34   0.001     .0669993    .2570777
           2013  |      .1653   .0521225     3.17   0.002     .0631417    .2674583
           2014  |    .183796   .0551517     3.33   0.001     .0757007    .2918912
                 |
           _cons |   .3968019   .0622796     6.37   0.000     .2747362    .5188676
-----------------+----------------------------------------------------------------
sanc_dur_dummy   |
  sanc_dur_dummy |
             L1. |    2.41492   .1700857    14.20   0.000     2.081559    2.748282
                 |
  pol_polity2dem |  -.9933571   .2648648    -3.75   0.000    -1.512483   -.4742316
     econ_lgdppc |   -.039571   .3081712    -0.13   0.898    -.6435754    .5644334
    econ_fdi_gdp |   .9983316   2.564032     0.39   0.697    -4.027079    6.023743
  econ_trade_gdp |   -.339089   .7088384    -0.48   0.632    -1.728387    1.050209
                 |
        iso3_num |
            ARG  |   .7113965   .9525541     0.75   0.455    -1.155575    2.578368
            ARM  |   6.524064   .5690899    11.46   0.000     5.408668     7.63946
            AZE  |    5.95069   .5678381    10.48   0.000     4.837748    7.063633
            BEN  |     1.2969   .9234614     1.40   0.160     -.513051    3.106851
            BGR  |   .7560631    .775091     0.98   0.329    -.7630874    2.275214
            BLR  |   5.755925   .5018559    11.47   0.000     4.772305    6.739544
            BOL  |   .9484423   .6951027     1.36   0.172     -.413934    2.310819
            BRA  |   .4045401   1.115749     0.36   0.717    -1.782287    2.591368
            CAN  |   1.799973    1.16486     1.55   0.122    -.4831099    4.083056
            CHE  |   .7952099   1.340349     0.59   0.553    -1.831827    3.422246
            CHN  |   6.456964    .805067     8.02   0.000     4.879062    8.034867
            CIV  |   5.570716   .5878564     9.48   0.000     4.418539    6.722893
            CMR  |  -1.036858   .7606623    -1.36   0.173    -2.527728     .454013
            COD  |   5.601949   .8462905     6.62   0.000      3.94325    7.260648
            COL  |   1.820493   .9677426     1.88   0.060    -.0762482    3.717233
            CRI  |   2.185807   .7660449     2.85   0.004     .6843867    3.687228
            CYP  |   7.593536   1.094395     6.94   0.000     5.448561    9.738511
            DOM  |   .9501505   .7736631     1.23   0.219    -.5662012    2.466502
            DZA  |  -5.145798   .5430549    -9.48   0.000    -6.210166    -4.08143
            ECU  |   .2963992   .6808876     0.44   0.663    -1.038116    1.630914
            EST  |   .7054749   .7978134     0.88   0.377    -.8582106     2.26916
            FRA  |   6.791375   1.144135     5.94   0.000     4.548911    9.033839
            GEO  |   .8720183   .6245789     1.40   0.163    -.3521338     2.09617
            HND  |   .7116893    .727952     0.98   0.328    -.7150705    2.138449
            HRV  |   .9019612   .7872513     1.15   0.252    -.6410231    2.444945
            IDN  |   1.743787   1.088671     1.60   0.109    -.3899687    3.877543
            IND  |   1.128516   .8342034     1.35   0.176    -.5064931    2.763524
            IRL  |   6.845238   1.268463     5.40   0.000     4.359097     9.33138
            IRN  |   6.804087   .7356838     9.25   0.000     5.362173    8.246001
            ISR  |   7.515599   1.203905     6.24   0.000     5.155988    9.875211
            ITA  |   .8627257   1.095478     0.79   0.431    -1.284372    3.009823
            KEN  |   .9149574   .7596315     1.20   0.228     -.573893    2.403808
            LBN  |   7.068863   .6311928    11.20   0.000     5.831748    8.305978
            LTU  |   1.062891   .7102001     1.50   0.134     -.329076    2.454857
            LVA  |   .4543866   .7354419     0.62   0.537    -.9870531    1.895826
            MDA  |   1.895217   .5339887     3.55   0.000     .8486179    2.941815
            MLI  |   .0931179   .8262134     0.11   0.910    -1.526231    1.712466
            MRT  |  -5.379131   .5412354    -9.94   0.000    -6.439933   -4.318329
            MWI  |   .4342742   1.028147     0.42   0.673    -1.580857    2.449405
            NGA  |   .7386872   .7629521     0.97   0.333    -.7566715    2.234046
            NOR  |   1.271971   1.329861     0.96   0.339    -1.334509    3.878451
            NPL  |  -.2104958   1.022281    -0.21   0.837    -2.214129    1.793138
            PAK  |   .6229484    .890917     0.70   0.484    -1.123217    2.369114
            PAN  |   1.208173   .6878153     1.76   0.079    -.1399203    2.556266
            PER  |  -.3579095   .7907511    -0.45   0.651    -1.907753    1.191934
            PHL  |   2.097551   .7026427     2.99   0.003     .7203962    3.474705
            PRY  |   .5379711   .8500539     0.63   0.527    -1.128104    2.204046
            ROU  |  -5.578245   .6663746    -8.37   0.000    -6.884315   -4.272174
            RUS  |   .4340594   .9045634     0.48   0.631    -1.338852    2.206971
            SDN  |   6.273389   .8485148     7.39   0.000     4.610331    7.936448
            SLE  |   .9498451   .9931793     0.96   0.339    -.9967506    2.896441
            THA  |   1.009155   .7131641     1.42   0.157    -.3886209    2.406931
            TUN  |   .2350965   .5075713     0.46   0.643     -.759725    1.229918
            TUR  |   .5268167   .8471092     0.62   0.534    -1.133487     2.18712
            UKR  |   .8187337   .7011464     1.17   0.243     -.555488    2.192955
            USA  |   1.764418   1.318521     1.34   0.181    -.8198367    4.348672
            VEN  |   1.019409   .8022926     1.27   0.204    -.5530553    2.591874
            ZAF  |   .3293214   .8097679     0.41   0.684    -1.257795    1.916437
                 |
            year |
           1998  |   .0304174   .3239906     0.09   0.925    -.6045925    .6654274
           1999  |  -.8745199   .3505008    -2.50   0.013    -1.561489   -.1875509
           2000  |  -.9231968    .367868    -2.51   0.012    -1.644205   -.2021889
           2001  |   .0943959   .3260473     0.29   0.772     -.544645    .7334368
           2002  |  -.1320611   .3006045    -0.44   0.660    -.7212351    .4571129
           2003  |   .6147148   .3251974     1.89   0.059    -.0226603     1.25209
           2004  |  -.0022675   .3531734    -0.01   0.995    -.6944746    .6899395
           2005  |    .345804   .3090413     1.12   0.263    -.2599059    .9515139
           2006  |   .3529157   .4191797     0.84   0.400    -.4686614    1.174493
           2007  |   -.065073   .3598286    -0.18   0.856    -.7703241     .640178
           2008  |   .3683201   .4190099     0.88   0.379    -.4529242    1.189564
           2009  |  -.0230985    .537551    -0.04   0.966    -1.076679    1.030482
           2010  |   .2103468   .4682888     0.45   0.653    -.7074824    1.128176
           2011  |   .7618308   .4624936     1.65   0.100      -.14464    1.668302
           2012  |   .3898933   .5254025     0.74   0.458    -.6398766    1.419663
           2013  |   .7874717   .4389009     1.79   0.073    -.0727582    1.647702
           2014  |   .5220685   .4720619     1.11   0.269    -.4031557    1.447293
                 |
           _cons |  -1.079339   2.444497    -0.44   0.659    -5.870466    3.711788
-----------------+----------------------------------------------------------------
         /athrho |  -.1652725   .0725044    -2.28   0.023    -.3073786   -.0231664
        /lnsigma |  -2.564783   .1041347   -24.63   0.000    -2.768883   -2.360683
-----------------+----------------------------------------------------------------
             rho |  -.1637839   .0705595                     -.2980504   -.0231623
           sigma |   .0769359   .0080117                       .062732    .0943558
          lambda |  -.0126009    .006206                     -.0247645   -.0004373
----------------------------------------------------------------------------------
Wald test of indep. eqns. (rho = 0): chi2(1) = 5.20       Prob > chi2 = 0.0226

.         est store sanc_dur_heckman_4

. 
.   * Compute rho from athrho
.     nlcom (rho: tanh(_b[/athrho]))

         rho: tanh(_b[/athrho])

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rho |  -.1637839   .0705595    -2.32   0.020     -.302078   -.0254899
------------------------------------------------------------------------------

.  
.   * Extract from r(table)
.     scalar rho_b  = r(table)[1,1]

.     scalar rho_se = r(table)[2,1]

.     scalar rho_p = r(table)[4,1] 

.         
.   * Restore stored model
.     est restore sanc_dur_heckman_4
(results sanc_dur_heckman_4 are active now)

. 
.   * Add them to esttab scalars
.     estadd scalar Rho = rho_b

added scalar:
                e(Rho) =  -.16378394

.     estadd scalar Rho_SE = rho_se

added scalar:
             e(Rho_SE) =  .0705595

.         estadd scalar Rho_p = rho_p

added scalar:
              e(Rho_p) =  .0202751

.     estadd scalar WaldChi2 = e(chi2_c)

added scalar:
           e(WaldChi2) =  5.1960331

.         estadd scalar Wald_p = e(p_c)

added scalar:
             e(Wald_p) =  .02263849

.         
.   * Export regression table             
.     esttab sanc_dur_heckman_1 sanc_dur_heckman_2 ///
>                sanc_dur_heckman_3 sanc_dur_heckman_4 ///
>                using "[Appendix 17] sanc_dur_heckman.rtf", ///
>                b(3) se(3) ///
>                scalars(N N_selected Rho Rho_SE Rho_p WaldChi2 Wald_p ll) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp ///
>                                  L.sanc_dur_dummy ///                            
>                  pol_polity2dem econ_lgdppc ///
>                                  econ_fdi_gdp econ_trade_gdp _cons ) ///
>                    keep(sanc_dur sanc_postdur ///
>                          sanc_fdur sanc_fpostdur ///
>                                  sanc_nonfdur sanc_nonfpostdur ///
>                      sanc_type sanc_state sanc_org ///
>                          econ_change_gdp econ_fin econ_asset_gdp ///
>                                  L.sanc_dur_dummy ///                            
>                   pol_polity2dem econ_lgdppc ///
>                                  econ_fdi_gdp econ_trade_gdp _cons ) ///
>                          replace        
(file [Appendix 17] sanc_dur_heckman.rtf not found)
(output written to [Appendix 17] sanc_dur_heckman.rtf)

. 
.         
. ** Appx. 18. Pre-1996: Included / Sample: 1-Year Lag / IV: Duration / OLS ******
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Model (1)   
.         reghdfe lerner l.sanc_dur l.sanc_postdur ///
>              l.sanc_type l.sanc_state l.sanc_org, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   5,   1729) =       7.75
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5460
                                                  Adj R-squared   =     0.5098
                                                  Within R-sq.    =     0.0109
                                                  Root MSE        =     0.1006

------------------------------------------------------------------------------
             |               Robust
      lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    sanc_dur |
         L1. |  -.0008691   .0007611    -1.14   0.254    -.0023618    .0006236
             |
sanc_postdur |
         L1. |   .0016909   .0015207     1.11   0.266    -.0012918    .0046736
             |
   sanc_type |
         L1. |    .012497   .0044764     2.79   0.005     .0037173    .0212767
             |
  sanc_state |
         L1. |  -.0091978   .0015826    -5.81   0.000    -.0123017   -.0060938
             |
    sanc_org |
         L1. |   .0018574   .0131392     0.14   0.888     -.023913    .0276278
             |
       _cons |   .2581707   .0040369    63.95   0.000      .250253    .2660883
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.         est store lag_dur_ols_1 

. 
.   * Model (2)   
.         reghdfe lerner l.sanc_dur l.sanc_postdur ///
>              l.sanc_type l.sanc_state l.sanc_org ///
>                  l.econ_change_gdp l.econ_fin l.econ_asset, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   8,   1726) =       6.14
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5534
                                                  Adj R-squared   =     0.5169
                                                  Within R-sq.    =     0.0270
                                                  Root MSE        =     0.0999

---------------------------------------------------------------------------------
                |               Robust
         lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
       sanc_dur |
            L1. |  -.0011323   .0007825    -1.45   0.148     -.002667    .0004024
                |
   sanc_postdur |
            L1. |   .0013819   .0015046     0.92   0.358     -.001569    .0043329
                |
      sanc_type |
            L1. |    .013817   .0045655     3.03   0.003     .0048626    .0227715
                |
     sanc_state |
            L1. |  -.0098023   .0016344    -6.00   0.000     -.013008   -.0065966
                |
       sanc_org |
            L1. |   .0010035   .0132968     0.08   0.940     -.025076     .027083
                |
econ_change_gdp |
            L1. |   .0030906   .0010761     2.87   0.004     .0009801    .0052012
                |
       econ_fin |
            L1. |   .0067058   .0069532     0.96   0.335    -.0069318    .0203434
                |
 econ_asset_gdp |
            L1. |  -.0003743   .0001506    -2.49   0.013    -.0006697   -.0000789
                |
          _cons |   .2680529   .0107913    24.84   0.000     .2468876    .2892182
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.         est store lag_dur_ols_2 

.         
.   * Model (3)   
.         reghdfe lerner l.sanc_fdur l.sanc_fpostdur ///
>              l.sanc_nonfdur l.sanc_nonfpostdur  ///
>              l.sanc_type l.sanc_state l.sanc_org, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   7,   1727) =       8.62
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5507
                                                  Adj R-squared   =     0.5143
                                                  Within R-sq.    =     0.0212
                                                  Root MSE        =     0.1002

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |
             L1. |   .0022735   .0011105     2.05   0.041     .0000954    .0044515
                 |
   sanc_fpostdur |
             L1. |  -.0062143     .00194    -3.20   0.001    -.0100192   -.0024094
                 |
    sanc_nonfdur |
             L1. |  -.0014372   .0009573    -1.50   0.133    -.0033149    .0004404
                 |
sanc_nonfpostdur |
             L1. |   .0009348   .0015608     0.60   0.549    -.0021266    .0039961
                 |
       sanc_type |
             L1. |    .005941   .0045769     1.30   0.194    -.0030359    .0149179
                 |
      sanc_state |
             L1. |  -.0081375    .001593    -5.11   0.000    -.0112619   -.0050132
                 |
        sanc_org |
             L1. |  -.0027997   .0130193    -0.22   0.830    -.0283349    .0227356
                 |
           _cons |   .2650816   .0042816    61.91   0.000     .2566839    .2734792
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.     est store lag_dur_ols_3 

.         
.   * Model (4)
.         reghdfe lerner l.sanc_fdur l.sanc_fpostdur ///
>              l.sanc_nonfdur l.sanc_nonfpostdur  ///
>              l.sanc_type l.sanc_state l.sanc_org ///
>                  l.econ_change_gdp l.econ_fin l.econ_asset, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(  10,   1724) =       7.49
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5580
                                                  Adj R-squared   =     0.5213
                                                  Within R-sq.    =     0.0370
                                                  Root MSE        =     0.0994

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |
             L1. |   .0022428    .001137     1.97   0.049     .0000128    .0044728
                 |
   sanc_fpostdur |
             L1. |  -.0062436   .0018927    -3.30   0.001    -.0099558   -.0025313
                 |
    sanc_nonfdur |
             L1. |  -.0015579   .0009824    -1.59   0.113    -.0034847    .0003689
                 |
sanc_nonfpostdur |
             L1. |   .0006852   .0015403     0.44   0.656    -.0023357    .0037062
                 |
       sanc_type |
             L1. |   .0070415   .0046473     1.52   0.130    -.0020734    .0161564
                 |
      sanc_state |
             L1. |  -.0086499   .0016498    -5.24   0.000    -.0118856   -.0054142
                 |
        sanc_org |
             L1. |  -.0037083   .0131865    -0.28   0.779    -.0295716     .022155
                 |
 econ_change_gdp |
             L1. |   .0031631   .0010769     2.94   0.003     .0010508    .0052753
                 |
        econ_fin |
             L1. |   .0024111   .0070403     0.34   0.732    -.0113973    .0162194
                 |
  econ_asset_gdp |
             L1. |  -.0003328   .0001497    -2.22   0.026    -.0006263   -.0000393
                 |
           _cons |   .2728628   .0107877    25.29   0.000     .2517045    .2940211
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        18           1          17     |
-----------------------------------------------------+

.     est store lag_dur_ols_4             

.         
.   * Export regression table                     
.     esttab lag_dur_ols_1  lag_dur_ols_2 ///
>                lag_dur_ols_3  lag_dur_ols_4 ///
>                using "[Appendix 18] lag_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(L.sanc_dur  L.sanc_postdur ///
>                          L.sanc_fdur L.sanc_fpostdur ///
>                                  L.sanc_nonfdur L.sanc_nonfpostdur ///             
>                      L.sanc_type L.sanc_state L.sanc_org ///
>                          L.econ_change_gdp L.econ_fin L.econ_asset_gdp) replace                 
(file [Appendix 18] lag_dur_ols.rtf not found)
(output written to [Appendix 18] lag_dur_ols.rtf)

. 
.         
. ** Appx. 19. Pre-1996: Included / Sample: Diff: 5-7 Yr / IV: Duration / OLS ****
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 4) by(iso3_num)   

.           
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff567 = (F5.lerner + F6.lerner + F7.lerner)/3 - lerner 
(821 missing values generated)

.         
.   * Model (1)
.         reghdfe lerner_diff567 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   5,   1047) =       2.74
                                                  Prob > F        =     0.0182
                                                  R-squared       =     0.2442
                                                  Adj R-squared   =     0.1503
                                                  Within R-sq.    =     0.0114
                                                  Root MSE        =     0.1360

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |    .013974   .0075793     1.84   0.066    -.0008983    .0288462
sanc_postdur_dummy_sum |  -.0009546    .005818    -0.16   0.870    -.0123708    .0104617
         sanc_type_max |  -.0019296   .0066497    -0.29   0.772    -.0149779    .0111187
        sanc_state_max |   .0047803   .0025529     1.87   0.061    -.0002291    .0097897
          sanc_org_max |  -.0250849   .0308775    -0.81   0.417    -.0856738     .035504
                 _cons |   .0222999   .0112171     1.99   0.047     .0002894    .0443104
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_1

.         
.   * Model (2)    
.         reghdfe lerner_diff567 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   8,   1044) =       2.66
                                                  Prob > F        =     0.0068
                                                  R-squared       =     0.2489
                                                  Adj R-squared   =     0.1532
                                                  Within R-sq.    =     0.0176
                                                  Root MSE        =     0.1358

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0148945   .0076879     1.94   0.053     -.000191    .0299801
sanc_postdur_dummy_sum |  -.0004264    .005818    -0.07   0.942    -.0118427      .01099
         sanc_type_max |  -.0023985   .0065196    -0.37   0.713    -.0151916    .0103946
        sanc_state_max |   .0046081   .0024804     1.86   0.063    -.0002591    .0094753
          sanc_org_max |  -.0240492   .0310741    -0.77   0.439     -.085024    .0369255
  econ_change_gdp_mean |  -.0031946   .0033123    -0.96   0.335    -.0096941     .003305
          econ_fin_max |   .0250537     .01431     1.75   0.080    -.0030259    .0531334
   econ_asset_gdp_mean |  -.0000734   .0003021    -0.24   0.808    -.0006662    .0005194
                 _cons |   .0284813   .0315656     0.90   0.367     -.033458    .0904207
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_2

.         
.   * Model (3)   
.         reghdfe lerner_diff567 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(   7,   1045) =       3.66
                                                  Prob > F        =     0.0006
                                                  R-squared       =     0.2503
                                                  Adj R-squared   =     0.1555
                                                  Within R-sq.    =     0.0194
                                                  Root MSE        =     0.1356

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0210299   .0094005     2.24   0.025      .002584    .0394759
   sanc_fpostdur_dummy_sum |   .0046367   .0098674     0.47   0.639    -.0147255    .0239988
    sanc_nonfdur_dummy_sum |   .0161443   .0077681     2.08   0.038     .0009015    .0313871
sanc_nonfpostdur_dummy_sum |  -.0041424   .0055404    -0.75   0.455     -.015014    .0067291
             sanc_type_max |  -.0111302   .0070565    -1.58   0.115    -.0249767    .0027164
            sanc_state_max |   .0054226   .0027248     1.99   0.047     .0000759    .0107694
              sanc_org_max |   -.033385    .035211    -0.95   0.343    -.1024774    .0357074
                     _cons |   .0193895   .0109548     1.77   0.077    -.0021063    .0408854
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_3

. 
.   * Model (4)   
.         reghdfe lerner_diff567 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,178
Absorbing 2 HDFE groups                           F(  10,   1042) =       3.21
                                                  Prob > F        =     0.0004
                                                  R-squared       =     0.2550
                                                  Adj R-squared   =     0.1584
                                                  Within R-sq.    =     0.0255
                                                  Root MSE        =     0.1354

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff567 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0240787   .0096146     2.50   0.012     .0052125    .0429449
   sanc_fpostdur_dummy_sum |   .0076883   .0101009     0.76   0.447    -.0121321    .0275088
    sanc_nonfdur_dummy_sum |   .0167848   .0078808     2.13   0.033     .0013208    .0322489
sanc_nonfpostdur_dummy_sum |  -.0024692     .00553    -0.45   0.655    -.0133204    .0083819
             sanc_type_max |  -.0113572   .0071279    -1.59   0.111    -.0253439    .0026295
            sanc_state_max |   .0052665   .0026781     1.97   0.050     .0000115    .0105216
              sanc_org_max |  -.0354217   .0354724    -1.00   0.318    -.1050273    .0341838
      econ_change_gdp_mean |  -.0028362   .0032655    -0.87   0.385     -.009244    .0035715
              econ_fin_max |   .0260155   .0142731     1.82   0.069    -.0019917    .0540227
       econ_asset_gdp_mean |  -.0000253   .0003048    -0.08   0.934    -.0006234    .0005729
                     _cons |   .0179506   .0291531     0.62   0.538    -.0392549    .0751561
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       115           0         115     |
        year |        12           1          11     |
-----------------------------------------------------+

.         est store diff567_dur_ols_4

. 
.   * Export regression table                     
.     esttab diff567_dur_ols_1 diff567_dur_ols_2 ///
>                diff567_dur_ols_3 diff567_dur_ols_4 ///
>                using "[Appendix 19] diff567_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace                 
(file [Appendix 19] diff567_dur_ols.rtf not found)
(output written to [Appendix 19] diff567_dur_ols.rtf)

.         
.         
. ** Appx. 20. Pre-1996: Included / Sample: Diff: 6-8 Yr / IV: Duration / OLS ****
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 5) by(iso3_num)   

. 
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff678 = (F6.lerner + F7.lerner + F8.lerner)/3 - lerner
(936 missing values generated)

.         
.   * Model (1)   
.         reghdfe lerner_diff678 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   5,    933) =       4.12
                                                  Prob > F        =     0.0010
                                                  R-squared       =     0.3701
                                                  Adj R-squared   =     0.2837
                                                  Within R-sq.    =     0.0191
                                                  Root MSE        =     0.1205

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0149469   .0071435     2.09   0.037     .0009277    .0289661
sanc_postdur_dummy_sum |  -.0003474   .0059202    -0.06   0.953    -.0119658    .0112711
         sanc_type_max |  -.0044056   .0068075    -0.65   0.518    -.0177654    .0089543
        sanc_state_max |   .0060402   .0024258     2.49   0.013     .0012796    .0108009
          sanc_org_max |  -.0019899   .0309425    -0.06   0.949     -.062715    .0587351
                 _cons |   .0222418   .0133715     1.66   0.097    -.0039999    .0484835
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_1

.         
.   * Model (2)    
.         reghdfe lerner_diff678 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   8,    930) =       3.03
                                                  Prob > F        =     0.0023
                                                  R-squared       =     0.3715
                                                  Adj R-squared   =     0.2830
                                                  Within R-sq.    =     0.0212
                                                  Root MSE        =     0.1206

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0154077   .0071718     2.15   0.032     .0013329    .0294825
sanc_postdur_dummy_sum |  -.0000413   .0059043    -0.01   0.994    -.0116286     .011546
         sanc_type_max |  -.0042965   .0067978    -0.63   0.528    -.0176372    .0090442
        sanc_state_max |    .005907   .0023869     2.47   0.014     .0012226    .0105914
          sanc_org_max |  -.0022614   .0309292    -0.07   0.942    -.0629605    .0584378
  econ_change_gdp_mean |  -.0008828   .0039257    -0.22   0.822    -.0085871    .0068215
          econ_fin_max |   .0157093   .0140034     1.12   0.262    -.0117726    .0431911
   econ_asset_gdp_mean |  -7.54e-06   .0003547    -0.02   0.983    -.0007037    .0006887
                 _cons |   .0182849   .0376817     0.49   0.628    -.0556661    .0922359
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_2

.         
.   * Model (3)   
.         reghdfe lerner_diff678 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(   7,    931) =       4.91
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3767
                                                  Adj R-squared   =     0.2897
                                                  Within R-sq.    =     0.0293
                                                  Root MSE        =     0.1200

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |    .022108   .0084799     2.61   0.009      .005466    .0387499
   sanc_fpostdur_dummy_sum |   .0053021   .0095682     0.55   0.580    -.0134756    .0240798
    sanc_nonfdur_dummy_sum |   .0148486   .0071926     2.06   0.039      .000733    .0289642
sanc_nonfpostdur_dummy_sum |  -.0033726   .0054868    -0.61   0.539    -.0141406    .0073953
             sanc_type_max |  -.0125947   .0071587    -1.76   0.079    -.0266438    .0014544
            sanc_state_max |   .0065526   .0025848     2.54   0.011       .00148    .0116253
              sanc_org_max |  -.0117393   .0329861    -0.36   0.722     -.076475    .0529964
                     _cons |   .0193763    .012463     1.55   0.120    -.0050826    .0438352
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_3

. 
.   * Model (4)   
.         reghdfe lerner_diff678 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(dropped 1 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      1,062
Absorbing 2 HDFE groups                           F(  10,    928) =       3.70
                                                  Prob > F        =     0.0001
                                                  R-squared       =     0.3782
                                                  Adj R-squared   =     0.2891
                                                  Within R-sq.    =     0.0317
                                                  Root MSE        =     0.1201

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff678 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0239267   .0087044     2.75   0.006     .0068441    .0410093
   sanc_fpostdur_dummy_sum |   .0065303   .0100415     0.65   0.516    -.0131764     .026237
    sanc_nonfdur_dummy_sum |   .0150384   .0072012     2.09   0.037     .0009059    .0291709
sanc_nonfpostdur_dummy_sum |  -.0024193   .0054118    -0.45   0.655    -.0130402    .0082016
             sanc_type_max |  -.0124823   .0072412    -1.72   0.085    -.0266934    .0017287
            sanc_state_max |   .0064702   .0025553     2.53   0.012     .0014554    .0114849
              sanc_org_max |   -.013725   .0331731    -0.41   0.679     -.078828     .051378
      econ_change_gdp_mean |  -.0002116   .0039495    -0.05   0.957    -.0079627    .0075395
              econ_fin_max |   .0171635   .0139832     1.23   0.220    -.0102789    .0446058
       econ_asset_gdp_mean |   .0000624   .0003563     0.18   0.861    -.0006369    .0007617
                     _cons |   .0061085   .0343737     0.18   0.859    -.0613507    .0735676
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        11           1          10     |
-----------------------------------------------------+

.         est store diff678_dur_ols_4

. 
.   * Export regression table                     
.     esttab diff678_dur_ols_1 diff678_dur_ols_2 ///
>                diff678_dur_ols_3 diff678_dur_ols_4 ///
>                using "[Appendix 20] diff678_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace                 
(file [Appendix 20] diff678_dur_ols.rtf not found)
(output written to [Appendix 20] diff678_dur_ols.rtf)

.         
.         
. ** Appx. 21. Pre-1996: Included / Sample: Diff: 7-9 Yr / IV: Duration / OLS ****
. 
. 
.   * Preparatory Steps (1): Basic setup
.     clear all

.     program drop _all

.     set more off

.     set mem 600m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. 
.   * Preparatory Steps (2): Import data
.         use "[Data] Pre-1996 Included.dta", replace     

.         
.   * Preparatory Steps (3): Declare panel 
.     encode iso3, gen(iso3_num)

.     xtset iso3_num year

Panel variable: iso3_num (strongly balanced)
 Time variable: year, 1996 to 2014
         Delta: 1 unit

.         
.   * Preparatory Steps (4): Run regression with minimum size sample
.         reghdfe lerner sanc_fdur sanc_fpostdur sanc_nonfdur sanc_nonfpostdur ///
>              sanc_type sanc_state sanc_org ///
>                  econ_change_gdp econ_fin econ_asset_gdp, ///
>                  absorb(iso3_num year) vce(robust)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,999
Absorbing 2 HDFE groups                           F(  10,   1854) =      10.22
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5430
                                                  Adj R-squared   =     0.5075
                                                  Within R-sq.    =     0.0443
                                                  Root MSE        =     0.1005

----------------------------------------------------------------------------------
                 |               Robust
          lerner | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       sanc_fdur |    .003897   .0010139     3.84   0.000     .0019085    .0058855
   sanc_fpostdur |  -.0061831   .0018969    -3.26   0.001    -.0099034   -.0024627
    sanc_nonfdur |  -.0026093   .0008713    -2.99   0.003    -.0043181   -.0009005
sanc_nonfpostdur |   .0001455   .0014511     0.10   0.920    -.0027006    .0029915
       sanc_type |  -.0008376   .0041321    -0.20   0.839    -.0089416    .0072664
      sanc_state |  -.0068626   .0015941    -4.30   0.000    -.0099892   -.0037361
        sanc_org |  -.0052814   .0152348    -0.35   0.729    -.0351606    .0245978
 econ_change_gdp |   .0033327   .0008978     3.71   0.000     .0015719    .0050935
        econ_fin |  -.0089928   .0071727    -1.25   0.210    -.0230602    .0050745
  econ_asset_gdp |  -.0002641   .0001414    -1.87   0.062    -.0005415    .0000132
           _cons |   .2739814   .0101905    26.89   0.000     .2539954    .2939674
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       117           0         117     |
        year |        19           1          18     |
-----------------------------------------------------+

.                  
.   * Preparatory Steps (5): Specify the minimum size sample
.     gen analysis_sample1 = e(sample)

. 
.   * Preparatory Steps (6): Restrict to minimum size sample
.     keep if analysis_sample1    
(1,022 observations deleted)

. 
.   * Preparatory Steps (7): Generate IV & CV     
.     rangestat (sum) sanc_dur_dummy sanc_postdur_dummy ///
>                     sanc_fdur_dummy sanc_fpostdur_dummy ///
>                     sanc_nonfdur_dummy sanc_nonfpostdur_dummy ///
>                                         sanc_dur sanc_postdur ///
>                                         sanc_fdur sanc_fpostdur ///
>                                         sanc_nonfpostdur sanc_nonfdur ///
>               (max) sanc_type sanc_state sanc_org econ_fin ///
>               (mean) econ_change_gdp econ_asset_gdp, ///
>               interval(year 0 6) by(iso3_num)   

. 
.   * Preparatory Steps (8): Calculate difference (DV)    
.     gen lerner_diff789 = (F7.lerner + F8.lerner + F9.lerner)/3 - lerner
(1,051 missing values generated)

. 
.   * Model (1)   
.         reghdfe lerner_diff789 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   5,    820) =       5.43
                                                  Prob > F        =     0.0001
                                                  R-squared       =     0.4521
                                                  Adj R-squared   =     0.3673
                                                  Within R-sq.    =     0.0252
                                                  Root MSE        =     0.1151

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0147945   .0067406     2.19   0.028     .0015636    .0280254
sanc_postdur_dummy_sum |  -.0003412   .0064978    -0.05   0.958    -.0130954    .0124131
         sanc_type_max |   .0014534   .0063782     0.23   0.820     -.011066    .0139729
        sanc_state_max |   .0037397   .0021589     1.73   0.084    -.0004978    .0079773
          sanc_org_max |   .0249443   .0295135     0.85   0.398    -.0329867    .0828753
                 _cons |   .0205407   .0156569     1.31   0.190    -.0101917     .051273
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_1

.         
.   * Model (2)    
.         reghdfe lerner_diff789 ///
>              sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   8,    817) =       3.44
                                                  Prob > F        =     0.0007
                                                  R-squared       =     0.4528
                                                  Adj R-squared   =     0.3657
                                                  Within R-sq.    =     0.0263
                                                  Root MSE        =     0.1153

----------------------------------------------------------------------------------------
                       |               Robust
        lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
    sanc_dur_dummy_sum |   .0149839   .0067284     2.23   0.026     .0017769     .028191
sanc_postdur_dummy_sum |  -.0002481    .006426    -0.04   0.969    -.0128615    .0123653
         sanc_type_max |   .0016424   .0064999     0.25   0.801    -.0111161     .014401
        sanc_state_max |   .0036903   .0022118     1.67   0.096    -.0006511    .0080317
          sanc_org_max |    .024962   .0297173     0.84   0.401    -.0333693    .0832933
  econ_change_gdp_mean |   .0019764   .0046715     0.42   0.672    -.0071932     .011146
          econ_fin_max |   .0118955   .0120597     0.99   0.324    -.0117761    .0355672
   econ_asset_gdp_mean |  -.0000867   .0003842    -0.23   0.822    -.0008407    .0006674
                 _cons |    .010541    .042988     0.25   0.806    -.0738389    .0949208
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_2

.         
.   * Model (3)   
.         reghdfe lerner_diff789 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max, ///
>                  absorb(iso3_num year) vce(robust)              
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(   7,    818) =       5.87
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4575
                                                  Adj R-squared   =     0.3719
                                                  Within R-sq.    =     0.0347
                                                  Root MSE        =     0.1147

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0197524   .0077159     2.56   0.011      .004607    .0348978
   sanc_fpostdur_dummy_sum |   .0038479   .0103013     0.37   0.709    -.0163723     .024068
    sanc_nonfdur_dummy_sum |   .0135037   .0067646     2.00   0.046     .0002256    .0267818
sanc_nonfpostdur_dummy_sum |  -.0034816   .0062008    -0.56   0.575    -.0156529    .0086898
             sanc_type_max |  -.0055669   .0067117    -0.83   0.407    -.0187411    .0076073
            sanc_state_max |   .0039171   .0023018     1.70   0.089    -.0006011    .0084353
              sanc_org_max |   .0179516   .0302021     0.59   0.552    -.0413312    .0772344
                     _cons |   .0195748   .0153984     1.27   0.204    -.0106502    .0497999
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_3

.         
.   * Model (4)
.         reghdfe lerner_diff789 ///
>              sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                  sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                  sanc_type_max sanc_state_max sanc_org_max ///
>                  econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean, ///
>                  absorb(iso3_num year) vce(robust)      
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        948
Absorbing 2 HDFE groups                           F(  10,    815) =       4.14
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4584
                                                  Adj R-squared   =     0.3706
                                                  Within R-sq.    =     0.0363
                                                  Root MSE        =     0.1148

--------------------------------------------------------------------------------------------
                           |               Robust
            lerner_diff789 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       sanc_fdur_dummy_sum |   .0202798   .0080202     2.53   0.012     .0045371    .0360225
   sanc_fpostdur_dummy_sum |   .0029457   .0109226     0.27   0.787     -.018494    .0243854
    sanc_nonfdur_dummy_sum |   .0135003    .006711     2.01   0.045     .0003275    .0266731
sanc_nonfpostdur_dummy_sum |  -.0029744   .0061326    -0.49   0.628    -.0150121    .0090632
             sanc_type_max |  -.0057276    .006886    -0.83   0.406     -.019244    .0077887
            sanc_state_max |   .0039703   .0023429     1.69   0.091    -.0006286    .0085692
              sanc_org_max |      .0178   .0308072     0.58   0.564    -.0426708    .0782708
      econ_change_gdp_mean |   .0030294   .0047465     0.64   0.524    -.0062874    .0123461
              econ_fin_max |   .0133619   .0119808     1.12   0.265     -.010155    .0368787
       econ_asset_gdp_mean |  -7.03e-06    .000389    -0.02   0.986    -.0007705    .0007564
                     _cons |  -.0001887   .0393733    -0.00   0.996    -.0774737    .0770963
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    iso3_num |       114           0         114     |
        year |        10           1           9     |
-----------------------------------------------------+

.         est store diff789_dur_ols_4

.         
.   * Export regression table             
.     esttab diff789_dur_ols_1 diff789_dur_ols_2 ///
>                diff789_dur_ols_3 diff789_dur_ols_4 ///
>                using "[Appendix 21] diff789_dur_ols.rtf", ///
>                b(3) se(3) ///
>                scalars(r2_a) ///
>                    star(* 0.1 ** 0.05 *** 0.01) ///               
>            nomtitles nobaselevels nogaps  ///
>            varwidth(15) modelwidth(10) ///
>                    order(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                          sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                          sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                          sanc_type_max sanc_state_max sanc_org_max ///
>                          econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                    keep(sanc_dur_dummy_sum sanc_postdur_dummy_sum ///
>                         sanc_fdur_dummy_sum sanc_fpostdur_dummy_sum ///
>                         sanc_nonfdur_dummy_sum sanc_nonfpostdur_dummy_sum  ///
>                         sanc_type_max sanc_state_max sanc_org_max ///
>                         econ_change_gdp_mean econ_fin_max econ_asset_gdp_mean _cons) ///
>                         replace                 
(file [Appendix 21] diff789_dur_ols.rtf not found)
(output written to [Appendix 21] diff789_dur_ols.rtf)

.         
. log close       
      name:  <unnamed>
       log:  C:\Users\hyuns\Desktop\[Material] Replication (v.3_single_do_file)\새 폴더\[Text] Log File (Tables and Figur
> es).log
  log type:  text
 closed on:  20 Nov 2025, 18:31:12
-------------------------------------------------------------------------------------------------------------------------
